add more changes

examples/dataset/action_tokenizer_example.py

@@ -0,0 +1,138 @@
+#!/usr/bin/env python
+
+"""
+Example demonstrating how to use the ActionTokenizerProcessorStep to tokenize actions.
+
+This example shows how to:
+1. Load a dataset with action data
+2. Apply the action tokenizer processor to tokenize actions with proper padding/truncation
+3. Access both the tokenized actions and the attention mask
+4. Decode tokenized actions back to their original form
+"""
+
+import torch
+from transformers import AutoProcessor
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.processor.core import EnvTransition, TransitionKey
+from lerobot.processor.tokenizer_processor import ActionTokenizerProcessorStep
+from lerobot.utils.constants import ACTION_TOKEN_MASK
+
+# Define delta timestamps for the dataset
+delta_timestamps = {
+    'action': [
+        0.0, 0.03333333333333333, 0.06666666666666667, 0.1, 0.13333333333333333,
+        0.16666666666666666, 0.2, 0.23333333333333334, 0.26666666666666666, 0.3,
+        0.3333333333333333, 0.36666666666666664, 0.4, 0.43333333333333335,
+        0.4666666666666667, 0.5, 0.5333333333333333, 0.5666666666666667, 0.6,
+        0.6333333333333333, 0.6666666666666666, 0.7, 0.7333333333333333,
+        0.7666666666666667, 0.8, 0.8333333333333334, 0.8666666666666667, 0.9,
+        0.9333333333333333, 0.9666666666666667, 1.0, 1.0333333333333334,
+        1.0666666666666667, 1.1, 1.1333333333333333, 1.1666666666666667, 1.2,
+        1.2333333333333334, 1.2666666666666666, 1.3, 1.3333333333333333,
+        1.3666666666666667, 1.4, 1.4333333333333333, 1.4666666666666666, 1.5,
+        1.5333333333333334, 1.5666666666666667, 1.6, 1.6333333333333333
+    ]
+}
+
+# Load the dataset
+print("Loading dataset...")
+dataset = LeRobotDataset(
+    repo_id="local",
+    root="/fsx/jade_choghari/outputs/pgen_annotations1",
+    delta_timestamps=delta_timestamps
+)
+
+# Create a dataloader
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    num_workers=0,
+    batch_size=4,
+    shuffle=True,
+)
+
+# Get a batch of data
+batch = next(iter(dataloader))
+action_data = batch["action"]  # Shape: (batch_size, action_horizon, action_dim)
+
+print(f"\nOriginal action shape: {action_data.shape}")
+print(f"Original action data (first sample, first timestep):\n{action_data[0, 0]}")
+
+# Method 1: Using the tokenizer directly (as in fast_tokenize.py)
+print("\n" + "="*80)
+print("Method 1: Direct tokenizer usage")
+print("="*80)
+
+tokenizer = AutoProcessor.from_pretrained("physical-intelligence/fast", trust_remote_code=True)
+
+# Tokenize directly
+tokens = tokenizer(action_data)
+print(f"\nDirect tokenization result type: {type(tokens)}")
+print(f"Tokens shape/length: {tokens.shape if isinstance(tokens, torch.Tensor) else len(tokens)}")
+
+# Decode
+decoded_actions = tokenizer.decode(tokens)
+print(f"Decoded actions shape: {decoded_actions.shape}")
+reconstruction_error = torch.abs(action_data - decoded_actions).mean()
+print(f"Mean absolute reconstruction error: {reconstruction_error.item():.6f}")
+
+# Method 2: Using the ActionTokenizerProcessorStep with proper padding/truncation
+print("\n" + "="*80)
+print("Method 2: Using ActionTokenizerProcessorStep (with padding & mask)")
+print("="*80)
+
+# Create the action tokenizer processor step
+action_tokenizer_processor = ActionTokenizerProcessorStep(
+    tokenizer_name="physical-intelligence/fast",
+    trust_remote_code=True,
+    max_action_tokens=32,  # Maximum number of tokens per action
+)
+
+# Create a transition with the action data
+transition = {
+    TransitionKey.ACTION: action_data,
+    TransitionKey.OBSERVATION: {},  # Empty for this example
+}
+
+# Apply the processor
+processed_transition = action_tokenizer_processor(transition)
+
+# Extract tokenized actions and mask
+tokenized_actions = processed_transition[TransitionKey.ACTION]
+complementary_data = processed_transition[TransitionKey.COMPLEMENTARY_DATA]
+action_mask = complementary_data[ACTION_TOKEN_MASK]
+
+print(f"\nTokenized actions shape: {tokenized_actions.shape}")  # (batch_size, max_action_tokens)
+print(f"Action mask shape: {action_mask.shape}")  # (batch_size, max_action_tokens)
+print(f"Tokenized actions dtype: {tokenized_actions.dtype}")
+print(f"Action mask dtype: {action_mask.dtype}")
+
+# Show token statistics
+print(f"\nFirst sample tokens: {tokenized_actions[0]}")
+print(f"First sample mask: {action_mask[0]}")
+num_real_tokens = action_mask[0].sum().item()
+print(f"Number of real tokens (non-padding): {num_real_tokens}")
+print(f"Number of padding tokens: {action_mask.shape[1] - num_real_tokens}")
+
+# Decode using the mask
+print("\nDecoding tokenized actions...")
+decoded_with_processor = tokenizer.decode(tokenized_actions)
+print(f"Decoded actions shape: {decoded_with_processor.shape}")
+
+# Calculate reconstruction error
+reconstruction_error_processor = torch.abs(action_data - decoded_with_processor).mean()
+print(f"Mean absolute reconstruction error: {reconstruction_error_processor.item():.6f}")
+
+# Show that masking works correctly
+print("\n" + "="*80)
+print("Mask demonstration")
+print("="*80)
+for i in range(min(4, tokenized_actions.shape[0])):
+    mask_i = action_mask[i]
+    num_real = mask_i.sum().item()
+    print(f"Sample {i}: {num_real} real tokens, {len(mask_i) - num_real} padding tokens")
+
+print("\n" + "="*80)
+print("Action tokenization example completed successfully!")
+print("="*80)
+

examples/dataset/annotate_pgen.py

@@ -1402,6 +1402,13 @@ def main():
         action="store_true",
         help="Push modified dataset to HuggingFace Hub",
     )
+    # add image key
+    parser.add_argument(
+        "--image-key",
+        type=str,
+        default=None,
+        help="Image observation key to use for image mode (default: None)",
+    )
     
     args = parser.parse_args()
     console = Console()
@@ -1443,7 +1450,10 @@ def main():
     )
     
     # Get image keys (for image mode)
-    image_keys = dataset.meta.camera_keys[:args.num_image_views_per_sample]
+    if args.image_key:
+        image_keys = [args.image_key]
+    else:
+        image_keys = dataset.meta.camera_keys[:args.num_image_views_per_sample]
     if not args.video_mode:
         console.print(f"[cyan]Using image keys: {image_keys}[/cyan]")
     

examples/dataset/fast_tokenize.py

@@ -0,0 +1,25 @@
+import numpy as np
+from transformers import AutoProcessor
+import torch
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+
+delta_timestamps = {'action': [0.0, 0.03333333333333333, 0.06666666666666667, 0.1, 0.13333333333333333, 0.16666666666666666, 0.2, 0.23333333333333334, 0.26666666666666666, 0.3, 0.3333333333333333, 0.36666666666666664, 0.4, 0.43333333333333335, 0.4666666666666667, 0.5, 0.5333333333333333, 0.5666666666666667, 0.6, 0.6333333333333333, 0.6666666666666666, 0.7, 0.7333333333333333, 0.7666666666666667, 0.8, 0.8333333333333334, 0.8666666666666667, 0.9, 0.9333333333333333, 0.9666666666666667, 1.0, 1.0333333333333334, 1.0666666666666667, 1.1, 1.1333333333333333, 1.1666666666666667, 1.2, 1.2333333333333334, 1.2666666666666666, 1.3, 1.3333333333333333, 1.3666666666666667, 1.4, 1.4333333333333333, 1.4666666666666666, 1.5, 1.5333333333333334, 1.5666666666666667, 1.6, 1.6333333333333333]}
+dataset = LeRobotDataset(repo_id="local", root="/fsx/jade_choghari/outputs/pgen_annotations1", delta_timestamps=delta_timestamps)
+
+dataloader = torch.utils.data.DataLoader(
+        dataset,
+        num_workers=0,
+        batch_size=4,
+        shuffle=True,
+)
+
+batch = next(iter(dataloader))
+
+# Load the tokenizer from the Hugging Face hub
+tokenizer = AutoProcessor.from_pretrained("physical-intelligence/fast", trust_remote_code=True)
+
+# Tokenize & decode action chunks (we use dummy data here)
+action_data = batch["action"]    # one batch of action chunks
+tokens = tokenizer(action_data)              # tokens = list[int]
+decoded_actions = tokenizer.decode(tokens)
+print("tokenized actions: ", tokens)

examples/dataset/inference_pi05.py

@@ -10,17 +10,19 @@ from lerobot.policies.factory import make_policy, make_policy_config
 from lerobot.configs.policies import PreTrainedConfig
 
 cfg = PreTrainedConfig.from_pretrained(
-    pretrained_name_or_path="/fsx/jade_choghari/outputs/pi0_training_new/checkpoints/last/pretrained_model",
+    pretrained_name_or_path="/fsx/jade_choghari/outputs/pi0_training/checkpoints/last/pretrained_model",
 )
 cfg.dtype = "bfloat16"
 
 pre_processor, post_processor = make_pre_post_processors(
     policy_cfg=cfg,
-    pretrained_path="/fsx/jade_choghari/outputs/pi0_training_new/checkpoints/last/pretrained_model",
+    pretrained_path="/fsx/jade_choghari/outputs/pi0_training/checkpoints/last/pretrained_model",
 )
 
+delta_timestamps = {'action': [0.0, 0.03333333333333333, 0.06666666666666667, 0.1, 0.13333333333333333, 0.16666666666666666, 0.2, 0.23333333333333334, 0.26666666666666666, 0.3, 0.3333333333333333, 0.36666666666666664, 0.4, 0.43333333333333335, 0.4666666666666667, 0.5, 0.5333333333333333, 0.5666666666666667, 0.6, 0.6333333333333333, 0.6666666666666666, 0.7, 0.7333333333333333, 0.7666666666666667, 0.8, 0.8333333333333334, 0.8666666666666667, 0.9, 0.9333333333333333, 0.9666666666666667, 1.0, 1.0333333333333334, 1.0666666666666667, 1.1, 1.1333333333333333, 1.1666666666666667, 1.2, 1.2333333333333334, 1.2666666666666666, 1.3, 1.3333333333333333, 1.3666666666666667, 1.4, 1.4333333333333333, 1.4666666666666666, 1.5, 1.5333333333333334, 1.5666666666666667, 1.6, 1.6333333333333333]}
+
+dataset = LeRobotDataset(repo_id="local", root="/fsx/jade_choghari/outputs/pgen_annotations1", delta_timestamps=delta_timestamps)
 
-dataset = LeRobotDataset(repo_id="local", root="/fsx/jade_choghari/outputs/pgen_annotations1")
 # rename map --rename_map='{
 #         "observation.images.side": "observation.images.base_0_rgb",
 #         "observation.images.up": "observation.images.left_wrist_0_rgb"
@@ -43,12 +45,12 @@ dataloader = torch.utils.data.DataLoader(
 )
 
 batch = next(iter(dataloader))
-
 batch = pre_processor(batch)
 policy.train()
 # run inference
 # action = policy.select_action(batch)
 loss, loss_dict = policy.forward(batch)
+breakpoint()
 # import requests
 # from PIL import Image
 # from transformers import AutoProcessor

examples/dataset/mask.md

@@ -0,0 +1,159 @@
+## One-sentence answer
+
+> `make_att_2d_masks(prefix_pad_masks, prefix_att_masks)` builds the **actual 2D attention mask** `[B, L, L]` that tells the transformer **which token positions may attend to which others**, combining **padding** and **causality**.
+
+Everything else you’ve seen so far was just metadata.
+
+---
+
+## What goes in
+
+### Inputs
+
+```python
+prefix_pad_masks   # shape [B, L]
+prefix_att_masks   # shape [B, L]
+```
+
+Where:
+
+* `prefix_pad_masks[b, i] = True`
+  → token `i` exists (not padding)
+
+* `prefix_att_masks[b, i] = False`
+  → token `i` is **bidirectional**
+
+* `prefix_att_masks[b, i] = True`
+  → token `i` is **causal (autoregressive)**
+
+---
+
+## What comes out
+
+```python
+att_2d_prefix  # shape [B, L, L]
+```
+
+Each entry:
+
+```text
+att_2d_prefix[b, i, j] = True
+```
+
+means:
+
+> “In batch `b`, **token i (query)** is allowed to attend to **token j (key)**.”
+
+---
+
+## How it is constructed (conceptually)
+
+For **each batch b**, **each query position i**, **each key position j**:
+
+```python
+if not prefix_pad_masks[b, j]:
+    att[b, i, j] = False           # cannot attend to padding
+else if not prefix_att_masks[b, i]:
+    att[b, i, j] = True            # bidirectional token → can see all real tokens
+else:
+    att[b, i, j] = (j <= i)        # causal token → can see only past + itself
+```
+
+That’s it.
+
+---
+
+## Tiny concrete example (exactly matching your code)
+
+Suppose:
+
+```python
+prefix_pad_masks[0] = [T, T, T, T, T, F]
+prefix_att_masks[0] = [F, F, F, T, T, T]
+```
+
+Tokens:
+
+```
+0: IMG
+1: IMG
+2: LANG
+3: SUB0
+4: SUB1
+5: PAD
+```
+
+---
+
+### Resulting `att_2d_prefix[0]`
+
+`✓ = True, ✗ = False`
+
+| Q \ K      | 0 | 1 | 2 | 3 | 4 | 5 |
+| ---------- | - | - | - | - | - | - |
+| 0 (bi)     | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ |
+| 1 (bi)     | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ |
+| 2 (bi)     | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ |
+| 3 (causal) | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ |
+| 4 (causal) | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ |
+| 5 (pad)    | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
+
+---
+
+## Why this matters for your training code
+
+This line:
+
+```python
+att_2d_prefix_4d = self._prepare_attention_masks_4d(att_2d_prefix)
+```
+
+Converts `[B, L, L] → [B, 1, L, L]` and possibly flips True/False to `0/-inf`.
+
+This is **exactly what Paligemma uses inside self-attention**.
+
+---
+
+## Key implications (VERY important)
+
+### 1️⃣ This mask does **not isolate token groups**
+
+* Bidirectional tokens can attend to **everything**
+* Causal tokens only restrict *their own row*
+
+So **flow/action tokens must be blocked separately**.
+
+---
+
+### 2️⃣ This is why your AR subtask prediction works
+
+* Subtask tokens are causal
+* Output at position `i` predicts token `i+1`
+* Padding is fully ignored
+
+---
+
+### 3️⃣ Inference behavior
+
+When `subtask_tokens = None`:
+
+* `prefix_att_masks` contains only `False`
+* `att_2d_prefix` becomes **fully bidirectional**
+* No AR behavior remains
+
+Exactly what you want.
+
+---
+
+## One-sentence takeaway (commit this)
+
+> `make_att_2d_masks` fuses **padding** and **causality** into a concrete `[B, L, L]` attention matrix that the transformer actually uses.
+
+If you want next, I can:
+
+* inspect `make_att_2d_masks()` source with you
+* show how to block **flow → subtask** attention
+* explain how this changes when suffix tokens are added
+* help you refactor this into a cleaner “grouped attention” API
+
+You’re now at the point where the model’s behavior should feel *predictable*, not magical.

examples/dataset/run.sh

@@ -1,10 +1,11 @@
+python examples/dataset/annotate.py \
+    --repo-id jadechoghari/collect-data \
+    --video-key observation.images.base \
+    --model Qwen/Qwen3-VL-30B-A3B-Instruct \
+    --episodes 16 22
+
 # python examples/dataset/annotate.py \
 #     --repo-id lerobot/svla_so101_pickplace \
 #     --video-key observation.images.side \
 #     --model Qwen/Qwen3-VL-30B-A3B-Instruct \
-
-python examples/dataset/annotate.py \
-    --repo-id lerobot/svla_so101_pickplace \
-    --video-key observation.images.side \
-    --model Qwen/Qwen3-VL-30B-A3B-Instruct \
-    --episodes 3 5 7 44
+#     --episodes 5

examples/dataset/run_pgen.sh

@@ -4,12 +4,12 @@
 # This generates user prompts and robot utterances for hierarchical policy training
 
 # Configuration
-REPO_ID="lerobot/svla_so101_pickplace"
+REPO_ID="jadechoghari/collect-data"
 MODEL="Qwen/Qwen3-VL-30B-A3B-Instruct"
 # Alternative: MODEL="Qwen/Qwen2-VL-7B-Instruct"
 
 
-OUTPUT_DIR="/fsx/jade_choghari/outputs/pgen_annotations1"
+OUTPUT_DIR="/fsx/jade_choghari/outputs/collect-data-pgen"
 BATCH_SIZE=32
 TEMPERATURE=0.9
 SAMPLE_INTERVAL=5.0  # Generate dialogue every 1 second (all episodes processed)
@@ -22,6 +22,7 @@ python examples/dataset/annotate_pgen.py \
     --temperature "$TEMPERATURE" \
     --batch-size "$BATCH_SIZE" \
     --sample-interval "$SAMPLE_INTERVAL" \
+    --image-key observation.images.base \
     --num-image-views-per-sample 1
 
 # For faster testing, increase sample interval:

examples/dataset/test.txt

@@ -0,0 +1 @@
+srun --time 12:00:00     --qos=high     --gres=gpu:1     --mem=24G     --partition=hopper-prod     --container-image /fsx/michel_aractingi/docker_images/huggingface+lerobot-gpu+dev.sqsh     --container-mounts /fsx/jade_choghari   

examples/voice_control/README.md

@@ -0,0 +1,47 @@
+# Voice Assistant Examples
+
+Voice-enabled robot assistant examples using speech-to-text (STT), and text-to-speech (TTS).
+
+## Overview
+
+These examples demonstrate how to build a voice interface for robot control:
+
+1. **Hold SPACE** → Push-to-talk recording starts
+2. **Release SPACE** → Recording stops
+3. **STT (Whisper)** → Converts speech to text (high-level task prompt)
+4. **Pi0.5** → Generates robot response/utterance
+5. **TTS (Kokoro)** → Speaks the response back
+
+## Requirements
+
+```bash
+pip install torch transformers sounddevice numpy pynput kokoro>=0.9.2
+```
+
+## Usage
+
+### With Pi0.5 Model
+
+```bash
+python examples/voice_assistant/voice_assistant_pi05.py \
+    --pretrained_path path/to/pi05/checkpoint
+```
+
+## How It Works
+
+### Pi0.5 Voice Integration
+
+Pi0.5 can generate robot utterances as part of its subtask prediction. The flow:
+
+1. **High-level prompt**: User voice command is transcribed and formatted as a task prompt
+2. **Subtask generation**: Pi0.5 autoregressively generates a response
+3. **Utterance extraction**: If the response contains `<utterance>...</utterance>` tags, the content is extracted
+4. **TTS output**: The response is spoken back to the user
+
+## Configuration Options
+
+| Option | Default | Description |
+|--------|---------|-------------|
+| `--pretrained_path` | None | Path to Pi0.5 checkpoint |
+| `--record_seconds` | 5.0 | Audio recording duration |
+| `--max_response_tokens` | 100 | Max tokens in generated response |

examples/voice_control/voice_assistant_pi05.py

@@ -0,0 +1,336 @@
+#!/usr/bin/env python
+"""
+Voice Assistant with Pi0.5: Microphone → STT → Pi0.5 → TTS → Speaker
+
+This example demonstrates how to use Pi0.5 as a conversational robot assistant:
+1. Hold SPACE to record your voice command
+2. Speech-to-text (Whisper) converts speech to text
+3. Text is fed as a high-level prompt to Pi0.5
+4. Pi0.5 generates a response (robot utterance)
+5. Text-to-speech (Kokoro) speaks the response back
+
+Requirements:
+    pip install torch transformers sounddevice numpy pynput kokoro>=0.9.2
+
+Usage:
+    python examples/voice_assistant/voice_assistant_pi05.py \
+        --pretrained_path lerobot/pi0.5-base
+"""
+
+import os
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+import argparse
+import re
+import subprocess
+import threading
+import time
+
+import numpy as np
+import sounddevice as sd
+import torch
+from pynput import keyboard
+from transformers import AutoTokenizer, WhisperForConditionalGeneration, WhisperProcessor
+
+from lerobot.policies.pi05.configuration_pi05 import PI05Config
+from lerobot.policies.pi05.modeling_pi05 import PI05Pytorch
+
+SAMPLE_RATE = 16000
+
+
+def get_device():
+    if torch.cuda.is_available():
+        return torch.device("cuda")
+    elif torch.backends.mps.is_available():
+        return torch.device("mps")
+    return torch.device("cpu")
+
+
+class Pi05VoiceAssistant:
+    """Voice assistant using Pi0.5 for generating robot utterances."""
+
+    def __init__(
+        self,
+        pretrained_path: str | None = None,
+        max_response_tokens: int = 100,
+        max_record_seconds: float = 30.0,
+    ):
+        self.device = get_device()
+        self.dtype = torch.float32 if self.device.type == "mps" else torch.bfloat16
+        self.max_response_tokens = max_response_tokens
+        self.max_record_seconds = max_record_seconds
+
+        # Push-to-talk state
+        self._recording = False
+        self._audio_chunks: list[np.ndarray] = []
+        self._stream: sd.InputStream | None = None
+
+        print(f"Using device: {self.device}")
+        self._load_models(pretrained_path)
+
+    def _load_models(self, pretrained_path: str | None):
+        print("Loading STT (Whisper tiny)...")
+        self.stt_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
+        self.stt_model = WhisperForConditionalGeneration.from_pretrained(
+            "openai/whisper-tiny.en", torch_dtype=self.dtype
+        ).to(self.device)
+
+        print("Loading Pi0.5 model...")
+        self._load_pi05(pretrained_path)
+
+        print("Loading tokenizer...")
+        self.tokenizer = AutoTokenizer.from_pretrained("google/paligemma-3b-pt-224")
+
+        self._load_tts()
+        print("Ready!\n")
+
+    def _load_pi05(self, pretrained_path: str | None):
+        """Load Pi0.5 model for utterance generation."""
+        config = PI05Config()
+        config.dtype = "float32" if self.device.type == "mps" else "bfloat16"
+
+        self.pi05_model = PI05Pytorch(config)
+
+        if pretrained_path:
+            try:
+                from safetensors.torch import load_file
+                state_dict = load_file(f"{pretrained_path}/model.safetensors")
+                self.pi05_model.load_state_dict(state_dict, strict=False)
+                print(f"✓ Loaded Pi0.5 weights from {pretrained_path}")
+            except Exception as e:
+                print(f"Warning: Could not load pretrained weights: {e}")
+                print("Using randomly initialized model for demo purposes")
+
+        self.pi05_model = self.pi05_model.to(self.device)
+        self.pi05_model.eval()
+
+    def _load_tts(self):
+        try:
+            print("Loading TTS (Kokoro 82M)...")
+            from kokoro import KPipeline
+
+            self.tts_pipeline = KPipeline(lang_code="a")  # American English
+            self.tts_voice = "af_heart"
+            self.tts_type = "kokoro"
+            print("Kokoro loaded!")
+        except Exception as e:
+            print(f"Kokoro not available ({e})")
+            print("Using macOS `say` for TTS")
+            self.tts_pipeline = None
+            self.tts_type = "system"
+
+    def _audio_callback(self, indata, frames, time_info, status):
+        """Callback for audio stream - collects chunks while recording."""
+        if self._recording:
+            self._audio_chunks.append(indata.copy())
+
+    def _start_recording(self):
+        """Start recording audio."""
+        if self._recording:
+            return
+        self._recording = True
+        self._audio_chunks = []
+        print("🎤 Recording... (release SPACE to stop)")
+
+    def _stop_recording(self) -> np.ndarray | None:
+        """Stop recording and return the audio."""
+        if not self._recording:
+            return None
+        self._recording = False
+
+        if not self._audio_chunks:
+            return None
+
+        audio = np.concatenate(self._audio_chunks, axis=0).flatten()
+        duration = len(audio) / SAMPLE_RATE
+        volume = np.abs(audio).max()
+        print(f"Recorded {duration:.1f}s, volume: {volume:.4f}")
+
+        if volume < 0.001:
+            print("⚠️  Very low audio - check microphone permissions!")
+            return None
+
+        return audio
+
+    def wait_for_spacebar(self) -> np.ndarray | None:
+        """Wait for spacebar press, record while held, return audio on release."""
+        audio_result = None
+        recording_done = threading.Event()
+
+        def on_press(key):
+            if key == keyboard.Key.space:
+                self._start_recording()
+
+        def on_release(key):
+            nonlocal audio_result
+            if key == keyboard.Key.space and self._recording:
+                audio_result = self._stop_recording()
+                recording_done.set()
+                return False  # Stop listener
+
+        # Start audio stream
+        self._stream = sd.InputStream(
+            samplerate=SAMPLE_RATE,
+            channels=1,
+            dtype="float32",
+            callback=self._audio_callback,
+            blocksize=int(SAMPLE_RATE * 0.1),  # 100ms blocks
+        )
+
+        with self._stream:
+            print("\n⏳ Press and hold SPACE to speak...")
+            with keyboard.Listener(on_press=on_press, on_release=on_release) as listener:
+                # Wait for recording to complete or timeout
+                recording_done.wait(timeout=self.max_record_seconds)
+                if self._recording:
+                    audio_result = self._stop_recording()
+
+        return audio_result
+
+    def transcribe(self, audio: np.ndarray) -> str:
+        start = time.perf_counter()
+        inputs = self.stt_processor(audio, sampling_rate=SAMPLE_RATE, return_tensors="pt")
+        input_features = inputs.input_features.to(self.device, dtype=self.dtype)
+        tokens = self.stt_model.generate(input_features)
+        text = self.stt_processor.batch_decode(tokens, skip_special_tokens=True)[0]
+        print(f"STT: {time.perf_counter() - start:.2f}s")
+        return text.strip()
+
+    def _create_dummy_images(self, batch_size: int = 1) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
+        """Create placeholder images for Pi0.5 when no camera is available."""
+        image_shape = (batch_size, 3, 224, 224)
+        dummy_image = torch.zeros(image_shape, dtype=torch.float32, device=self.device)
+        dummy_mask = torch.ones(batch_size, dtype=torch.bool, device=self.device)
+        return [dummy_image], [dummy_mask]
+
+    def _tokenize_prompt(self, text: str) -> tuple[torch.Tensor, torch.Tensor]:
+        """Tokenize the user prompt for Pi0.5."""
+        prompt = f"User request: {text}\nRobot response:"
+        tokenized = self.tokenizer(
+            [prompt],
+            max_length=200,
+            truncation=True,
+            padding="max_length",
+            return_tensors="pt",
+        )
+        tokens = tokenized["input_ids"].to(self.device)
+        masks = tokenized["attention_mask"].to(self.device, dtype=torch.bool)
+        return tokens, masks
+
+    def generate_response(self, user_text: str) -> str:
+        """Generate robot utterance using Pi0.5's language generation."""
+        start = time.perf_counter()
+
+        images, img_masks = self._create_dummy_images()
+        tokens, masks = self._tokenize_prompt(user_text)
+
+        with torch.no_grad():
+            generated_tokens = self.pi05_model._generate_subtask_tokens(
+                images=images,
+                img_masks=img_masks,
+                tokens=tokens,
+                masks=masks,
+                tokenizer=self.tokenizer,
+                max_length=self.max_response_tokens,
+                device=self.device,
+            )
+
+        # Decode generated tokens
+        valid_tokens = generated_tokens[0][generated_tokens[0] != 0]
+        response = self.tokenizer.decode(valid_tokens, skip_special_tokens=True)
+
+        # Extract utterance if marked with special tokens
+        response = self._extract_utterance(response)
+
+        print(f"Pi0.5: {time.perf_counter() - start:.2f}s")
+        return response.strip()
+
+    def _extract_utterance(self, text: str) -> str:
+        """Extract utterance from between <utterance> tokens if present."""
+        pattern = r"<utterance>(.*?)</utterance>"
+        match = re.search(pattern, text, re.DOTALL)
+        if match:
+            return match.group(1).strip()
+        return text
+
+    def speak(self, text: str):
+        start = time.perf_counter()
+        if self.tts_type == "kokoro":
+            generator = self.tts_pipeline(text, voice=self.tts_voice)
+            audio_chunks = [audio for _, _, audio in generator]
+            if audio_chunks:
+                audio = np.concatenate(audio_chunks)
+                sd.play(audio, 24000)
+                sd.wait()
+        else:
+            subprocess.run(["say", text], check=True)
+        print(f"TTS: {time.perf_counter() - start:.2f}s")
+
+    def run(self):
+        print("=" * 50)
+        print("Pi0.5 Voice Assistant")
+        print("=" * 50)
+        print("• Hold SPACE to record your voice command")
+        print("• Release SPACE when done speaking")
+        print("• Press Ctrl+C to exit")
+        print("=" * 50)
+
+        while True:
+            try:
+                audio = self.wait_for_spacebar()
+
+                if audio is None:
+                    print("(no audio captured)\n")
+                    continue
+
+                user_text = self.transcribe(audio)
+
+                if not user_text:
+                    print("(no speech detected)\n")
+                    continue
+
+                print(f"You: {user_text}")
+
+                response = self.generate_response(user_text)
+                print(f"Robot: {response}\n")
+
+                self.speak(response)
+
+            except KeyboardInterrupt:
+                print("\nGoodbye!")
+                break
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Pi0.5 Voice Assistant")
+    parser.add_argument(
+        "--pretrained_path",
+        type=str,
+        default=None,
+        help="Path to pretrained Pi0.5 model (optional)",
+    )
+    parser.add_argument(
+        "--max_response_tokens",
+        type=int,
+        default=100,
+        help="Maximum tokens in generated response",
+    )
+    parser.add_argument(
+        "--max_record_seconds",
+        type=float,
+        default=30.0,
+        help="Maximum recording duration in seconds",
+    )
+    args = parser.parse_args()
+
+    assistant = Pi05VoiceAssistant(
+        pretrained_path=args.pretrained_path,
+        max_response_tokens=args.max_response_tokens,
+        max_record_seconds=args.max_record_seconds,
+    )
+    assistant.run()
+
+
+if __name__ == "__main__":
+    main()

fast_tokenizer_local/metadata.json

@@ -0,0 +1,26 @@
+{
+  "repo_id": "local",
+  "vocab_size": 1024,
+  "scale": 10.0,
+  "encoded_dims": "0:15",
+  "encoded_dim_ranges": [
+    [
+      0,
+      15
+    ]
+  ],
+  "total_encoded_dims": 15,
+  "delta_dims": null,
+  "delta_dim_list": null,
+  "use_delta_transform": false,
+  "state_key": "observation.state",
+  "action_horizon": 50,
+  "num_training_chunks": 4900,
+  "compression_stats": {
+    "compression_ratio": 15.85791309863622,
+    "mean_token_length": 47.295,
+    "p99_token_length": 90.0,
+    "min_token_length": 9.0,
+    "max_token_length": 109.0
+  }
+}

fast_tokenizer_local/processing_action_tokenizer.py

@@ -0,0 +1,158 @@
+import logging
+from typing import ClassVar
+
+import numpy as np
+from scipy.fft import dct
+from scipy.fft import idct
+from tokenizers import ByteLevelBPETokenizer
+from tokenizers.trainers import BpeTrainer
+from transformers import PreTrainedTokenizerFast
+from transformers.processing_utils import ProcessorMixin
+
+
+class UniversalActionProcessor(ProcessorMixin):
+    attributes: ClassVar[list[str]] = ["bpe_tokenizer"]
+    bpe_tokenizer_class: str = "AutoTokenizer"
+
+    def __init__(
+        self,
+        bpe_tokenizer: PreTrainedTokenizerFast,
+        scale: float = 10,
+        vocab_size: int = 1024,
+        min_token: int = 0,
+        *,
+        action_dim: int | None = None,
+        time_horizon: int | None = None,
+    ):
+        self.scale = scale
+        self.vocab_size = vocab_size
+        self.min_token = min_token
+
+        # Action horizon and dimension needed during decoding. These can be specified
+        # in three ways (in order of priority):
+        # 1. passed in as kwargs to decode()
+        # 2. in the constructor
+        # 3. cached from the last time decode() was called
+        self.time_horizon = time_horizon
+        self.action_dim = action_dim
+        self.called_time_horizon = time_horizon
+        self.called_action_dim = action_dim
+
+        super().__init__(bpe_tokenizer)
+
+    def __call__(self, action_chunk: np.array) -> np.array:
+        assert action_chunk.ndim <= 3, "Only 3 dimensions supported: [batch, timesteps, action_dim]"
+        if action_chunk.ndim == 2:
+            action_chunk = action_chunk[None, ...]
+
+        # Cache the time horizon and action dimension for decoding
+        self.called_time_horizon = action_chunk.shape[-2]
+        self.called_action_dim = action_chunk.shape[-1]
+
+        dct_coeff = dct(action_chunk, axis=1, norm="ortho")
+        dct_coeff = np.around(dct_coeff * self.scale)
+        tokens = []
+        for elem in dct_coeff:
+            token_str = "".join(map(chr, np.maximum(elem.flatten() - self.min_token, 0).astype(int)))
+            tokens.append(self.bpe_tokenizer(token_str)["input_ids"])
+        return tokens
+
+    def decode(
+        self,
+        tokens: list[list[int]],
+        *,
+        time_horizon: int | None = None,
+        action_dim: int | None = None,
+    ) -> np.array:
+        self.time_horizon = time_horizon or self.time_horizon or self.called_time_horizon
+        self.action_dim = action_dim or self.action_dim or self.called_action_dim
+
+        # Cache the time horizon and action dimension for the next call
+        self.called_time_horizon = self.time_horizon
+        self.called_action_dim = self.action_dim
+
+        assert (
+            self.time_horizon is not None and self.action_dim is not None
+        ), "Tokenizer not initialized, call encode() once or pass in time_horizon and action_dim."
+
+        decoded_actions = []
+        for token in tokens:
+            try:
+                decoded_tokens = self.bpe_tokenizer.decode(token)
+                decoded_dct_coeff = np.array(list(map(ord, decoded_tokens))) + self.min_token
+                decoded_dct_coeff = decoded_dct_coeff.reshape(-1, self.action_dim)
+                assert (
+                    decoded_dct_coeff.shape
+                    == (
+                        self.time_horizon,
+                        self.action_dim,
+                    )
+                ), f"Decoded DCT coefficients have shape {decoded_dct_coeff.shape}, expected ({self.time_horizon}, {self.action_dim})"
+            except Exception as e:
+                print(f"Error decoding tokens: {e}")
+                print(f"Tokens: {token}")
+                decoded_dct_coeff = np.zeros((self.time_horizon, self.action_dim))
+            decoded_actions.append(idct(decoded_dct_coeff / self.scale, axis=0, norm="ortho"))
+        return np.stack(decoded_actions)
+
+    @classmethod
+    def fit(
+        cls,
+        action_data: list[np.array],
+        scale: float = 10,
+        vocab_size: int = 1024,
+        *,
+        time_horizon: int | None = None,
+        action_dim: int | None = None,
+    ) -> "UniversalActionProcessor":
+        # Run DCT over all inputs
+        dct_tokens = [dct(a, axis=0, norm="ortho").flatten() for a in action_data]
+
+        # Quantize and find min token
+        max_token = int(np.around(np.concatenate(dct_tokens) * scale).max())
+        min_token = int(np.around(np.concatenate(dct_tokens) * scale).min())
+        min_vocab_size = max_token - min_token
+
+        assert (
+            min_vocab_size <= vocab_size
+        ), f"Vocab size {vocab_size} is too small for the range of tokens {min_vocab_size}"
+        if min_vocab_size + 100 > vocab_size:
+            logging.warning(
+                f"Initial alphabet size {min_vocab_size} is almost as large as the vocab"
+                f"size {vocab_size}, consider increasing vocab size"
+            )
+
+        # Make token iterator for BPE training
+        def _token_iter():
+            for tokens in dct_tokens:
+                rounded_tokens = np.around(tokens * scale) - min_token
+                rounded_tokens = rounded_tokens.astype(int)
+                string = "".join(map(chr, rounded_tokens))
+                yield string
+
+        # Train BPE tokenizer
+        bpe = ByteLevelBPETokenizer()
+
+        # Set up the entire range of possible tokens as the initial alphabet
+        alphabet = [chr(i) for i in range(max_token - min_token + 1)]
+        trainer = BpeTrainer(
+            vocab_size=vocab_size,
+            min_frequency=2,
+            show_progress=True,
+            special_tokens=[],
+            initial_alphabet=alphabet,
+            max_token_length=10000,
+        )
+
+        # Train the inner tokenizer (don't use ByteLevelBPETokenizer.train_from_iterator()
+        # because it doesn't support custom alphabets)
+        bpe._tokenizer.train_from_iterator(_token_iter(), trainer=trainer)
+
+        return cls(
+            PreTrainedTokenizerFast(tokenizer_object=bpe, clean_up_tokenization_spaces=False),
+            scale=scale,
+            vocab_size=vocab_size,
+            min_token=min_token,
+            time_horizon=time_horizon,
+            action_dim=action_dim,
+        )

fast_tokenizer_local/processor_config.json

@@ -0,0 +1,11 @@
+{
+  "action_dim": 15,
+  "auto_map": {
+    "AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
+  },
+  "min_token": -71,
+  "processor_class": "UniversalActionProcessor",
+  "scale": 10.0,
+  "time_horizon": 50,
+  "vocab_size": 1024
+}

fast_tokenizer_local/special_tokens_map.json

@@ -0,0 +1 @@
+{}

fast_tokenizer_local/tokenizer_config.json

@@ -0,0 +1,11 @@
+{
+  "added_tokens_decoder": {},
+  "auto_map": {
+    "AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
+  },
+  "clean_up_tokenization_spaces": false,
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "processor_class": "UniversalActionProcessor",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

src/lerobot/policies/pi05/README_TOKENIZER.md

@@ -0,0 +1,196 @@
+# FAST Tokenizer Training for LeRobotDataset
+
+This directory contains tools for training a FAST (Factorized Action Sequence Tokenizer) on LeRobot datasets.
+
+## Files
+
+- **`train_fast_tokenizer.py`**: Main training script (refactored for LeRobotDataset)
+- **`train_fast_tokenizer_example.md`**: Usage examples and parameter documentation
+- **`MIGRATION_NOTES.md`**: Migration guide from B1K to LeRobotDataset
+
+## Quick Start
+
+```bash
+# Basic usage
+python train_fast_tokenizer.py \
+    --repo_id "lerobot/aloha_sim_insertion_human" \
+    --action_horizon 10 \
+    --encoded_dims "0:14"
+
+# With delta transform
+python train_fast_tokenizer.py \
+    --repo_id "lerobot/aloha_sim_insertion_human" \
+    --action_horizon 10 \
+    --encoded_dims "0:14" \
+    --delta_dims "0,1,2,3,4,5,6,7,8,9,10,11,12,13" \
+    --state_key "observation.state" \
+    --vocab_size 1024
+```
+
+## What is FAST?
+
+FAST is a tokenizer for robotic action sequences that:
+1. Applies DCT (Discrete Cosine Transform) to action chunks
+2. Quantizes DCT coefficients 
+3. Uses BPE (Byte-Pair Encoding) to compress the quantized sequence
+4. Achieves high compression ratios (e.g., 10-20x) while maintaining accuracy
+
+This enables efficient storage and processing of long action sequences in vision-language-action models.
+
+## Requirements
+
+- Python 3.10+
+- LeRobot dataset (either local or from HuggingFace Hub)
+- transformers (for AutoProcessor)
+- numpy
+- torch
+- tyro
+
+## Workflow
+
+```
+LeRobotDataset → Extract Episodes → Apply Delta Transform 
+    ↓
+Select Dimensions → Normalize (q01, q99) → Create Chunks
+    ↓
+Train FAST Tokenizer → Compute Stats → Save
+```
+
+## Parameters Guide
+
+### Essential Parameters
+
+- **`repo_id`**: HuggingFace dataset repository ID
+  - Example: `"lerobot/aloha_sim_insertion_human"`
+  
+- **`action_horizon`**: Length of action sequences to tokenize
+  - Typical: 10-16 steps
+  
+- **`encoded_dims`**: Which action dimensions to encode
+  - Format: `"start:end,start:end"`
+  - Example: `"0:7"` = dimensions 0-6
+  - Example: `"0:3,7:10"` = dimensions 0-2 and 7-9
+
+### Optional Parameters
+
+- **`delta_dims`**: Apply delta transform (action - state) to these dimensions
+  - Format: `"0,1,2,3,4,5"`
+  - Use for position-based actions
+  
+- **`state_key`**: Dataset key containing state observations
+  - Default: `"observation.state"`
+  
+- **`vocab_size`**: BPE vocabulary size
+  - Default: 1024
+  - Larger = better compression but more memory
+  
+- **`scale`**: DCT quantization scale
+  - Default: 10.0
+  - Smaller = finer quantization, larger = coarser
+
+- **`sample_fraction`**: Fraction of action chunks to use per episode
+  - Default: 0.1 (10%)
+  - Increase for small datasets, decrease for large datasets
+
+## Output
+
+The script creates a directory (default: `./fast_tokenizer_{repo_id}`) containing:
+
+1. **Tokenizer files**: Can be loaded with `AutoProcessor.from_pretrained()`
+2. **`metadata.json`**: Contains:
+   - Training configuration
+   - Compression statistics
+   - Dataset information
+
+## Example Output
+
+```
+Loading dataset: lerobot/aloha_sim_insertion_human
+Dataset loaded: 50 episodes, 5000 frames
+Encoding 14 dimensions: 0:14
+Delta dimensions: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
+Action horizon: 10
+Processing 50 episodes...
+Collected 4500 action chunks
+Extracted 14 encoded dimensions
+
+Before normalization - overall stats:
+  Min: -2.3451, Max: 3.1234, Mean: 0.0234, Std: 0.8765
+
+Applied quantile normalization [q01, q99] → [-1, 1]
+
+After normalization - overall stats:
+  Min: -1.0000, Max: 1.0000, Mean: 0.0156, Std: 0.4321
+
+Training FAST tokenizer on 4500 action chunks...
+Action chunk shape: (4500, 10, 14)
+Vocab size: 1024
+DCT scale: 10.0
+✓ Tokenizer training complete!
+
+Compression Statistics:
+  Average compression ratio: 14.23x
+  Mean token length: 9.8
+  P99 token length: 15
+  Min token length: 6
+  Max token length: 18
+
+✅ Saved FAST tokenizer to ./fast_tokenizer_lerobot_aloha_sim_insertion_human
+```
+
+## Using the Trained Tokenizer
+
+```python
+from transformers import AutoProcessor
+
+# Load tokenizer
+tokenizer = AutoProcessor.from_pretrained(
+    "./fast_tokenizer_lerobot_aloha_sim_insertion_human",
+    trust_remote_code=True
+)
+
+# Encode action chunk [horizon, action_dim]
+action_chunk = np.random.randn(10, 14)  # Example
+tokens = tokenizer(action_chunk[None])[0]  # Returns token IDs
+
+# Decode tokens back to actions
+reconstructed = tokenizer.decode(tokens)
+```
+
+## Tips
+
+1. **Start Small**: Use `--max_episodes 10` for initial testing
+2. **Check Dimensions**: Verify encoded dimensions match your robot's action space
+3. **Delta Transform**: Use for position-based actions, not velocity-based
+4. **Normalization**: Ensure dataset has proper statistics computed
+5. **Compression Ratio**: Aim for 10-20x for good balance of compression and accuracy
+
+## Troubleshooting
+
+**Issue**: "No normalization stats found"
+- **Solution**: Compute dataset statistics first, or use raw actions
+
+**Issue**: "Episode too short for action horizon"
+- **Solution**: Reduce `--action_horizon` or filter short episodes
+
+**Issue**: "State key not found"
+- **Solution**: Check dataset features and use correct `--state_key`
+
+**Issue**: Memory error with large datasets
+- **Solution**: Reduce `--sample_fraction` or `--max_episodes`
+
+## Citation
+
+If you use FAST in your research, please cite:
+
+```bibtex
+@article{black2023fast,
+  title={FAST: Factorized Action Sequence Tokenizer for Vision-Language-Action Models},
+  author={Black, Kevin and others},
+  journal={arXiv preprint},
+  year={2023}
+}
+```
+
+
+

src/lerobot/policies/pi05/configuration_pi05.py

@@ -37,6 +37,9 @@ class PI05Config(PreTrainedConfig):
     # Shorter state and action vectors will be padded to these dimensions
     max_state_dim: int = 32
     max_action_dim: int = 32
+    max_action_tokens: int = 32
+    fast_vocab_size: int = 2048
+    
 
     # Flow matching parameters: see openpi `PI0Pytorch`
     num_inference_steps: int = 10

src/lerobot/policies/pi05/finetune_pi0.sh

@@ -0,0 +1,21 @@
+lerobot-train \
+    --dataset.repo_id=lerobot \
+    --dataset.root=/fsx/jade_choghari/outputs/collect-data-pgen \
+    --output_dir=/fsx/jade_choghari/outputs/pi0test1 \
+    --job_name=pi0_training \
+    --policy.repo_id=jade_choghari/pi0-base \
+    --policy.path=/fsx/jade_choghari/outputs/pi0_fast_fruit1/checkpoints/last/pretrained_model \
+    --policy.dtype=bfloat16 \
+    --steps=3000 \
+    --save_freq=1000 \
+    --rename_map='{
+        "observation.images.base": "observation.images.base_0_rgb",
+        "observation.images.left_wrist": "observation.images.left_wrist_0_rgb",
+        "observation.images.right_wrist": "observation.images.right_wrist_0_rgb",
+        }' \
+    --batch_size=4 \
+    --policy.device=cuda \
+    # --wandb.enable=true \
+    # --wandb.disable_artifact=true \
+    # --wandb.project=pi05hi-training \
+

src/lerobot/policies/pi05/modeling_pi05.py

@@ -537,6 +537,18 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         self.time_mlp_in = nn.Linear(action_expert_config.width, action_expert_config.width)
         self.time_mlp_out = nn.Linear(action_expert_config.width, action_expert_config.width)
 
+        # FAST action token embedding and prediction head
+        self.fast_action_embedding = nn.Embedding(config.fast_vocab_size, paligemma_config.width)
+        self.fast_action_lm_head = nn.Linear(paligemma_config.width, config.fast_vocab_size)
+
+        # Apply dtype conversion to FAST layers to match model precision
+        if config.dtype == "bfloat16":
+            self.fast_action_embedding = self.fast_action_embedding.to(dtype=torch.bfloat16)
+            self.fast_action_lm_head = self.fast_action_lm_head.to(dtype=torch.bfloat16)
+        elif config.dtype == "float32":
+            self.fast_action_embedding = self.fast_action_embedding.to(dtype=torch.float32)
+            self.fast_action_lm_head = self.fast_action_lm_head.to(dtype=torch.float32)
+
         # Initialize gradient checkpointing flag
         self.gradient_checkpointing_enabled = False
 
@@ -592,6 +604,194 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             result = result.to(dtype=dtype)
         return result
 
+    def _create_custom_attention_mask(self, att_mask_segments, pad_masks, bsize):
+        """Create custom 2D attention mask for the new attention pattern.
+        
+        Attention rules:
+        - Images + Language: bidirectional among themselves, don't attend to subtask or FAST
+        - Subtask: attend to images + language, causal among themselves, don't attend to FAST
+        - FAST: attend to images + language + subtask, causal among themselves
+        
+        Args:
+            att_mask_segments: List of (type, length) tuples
+            pad_masks: Padding masks [B, total_seq_len]
+            bsize: Batch size
+        
+        Returns:
+            att_2d_masks: 2D attention mask [B, total_seq_len, total_seq_len]
+        """
+        total_len = sum(length for _, length in att_mask_segments)
+        device = pad_masks.device
+        
+        # Initialize attention mask as False (cannot attend)
+        att_2d_masks = torch.zeros(bsize, total_len, total_len, dtype=torch.bool, device=device)
+        
+        # Track positions for each segment
+        positions = []
+        current_pos = 0
+        for seg_type, seg_len in att_mask_segments:
+            positions.append((seg_type, current_pos, current_pos + seg_len))
+            current_pos += seg_len
+        
+        # Apply attention rules
+        for i, (query_type, query_start, query_end) in enumerate(positions):
+            for j, (key_type, key_start, key_end) in enumerate(positions):
+                # Images and Language can attend to each other bidirectionally
+                if query_type in ['image', 'language'] and key_type in ['image', 'language']:
+                    att_2d_masks[:, query_start:query_end, key_start:key_end] = True
+                
+                # Subtask tokens attend to images + language
+                elif query_type == 'subtask' and key_type in ['image', 'language']:
+                    att_2d_masks[:, query_start:query_end, key_start:key_end] = True
+                
+                # Subtask tokens attend causally to themselves
+                elif query_type == 'subtask' and key_type == 'subtask':
+                    # Create causal mask for subtask tokens
+                    subtask_len = query_end - query_start
+                    causal_mask = torch.tril(torch.ones(subtask_len, subtask_len, dtype=torch.bool, device=device))
+                    att_2d_masks[:, query_start:query_end, key_start:key_end] = causal_mask[None, :, :]
+                
+                # FAST tokens attend to images + language + subtask
+                elif query_type == 'fast' and key_type in ['image', 'language', 'subtask']:
+                    att_2d_masks[:, query_start:query_end, key_start:key_end] = True
+                
+                # FAST tokens attend causally to themselves
+                elif query_type == 'fast' and key_type == 'fast':
+                    fast_len = query_end - query_start
+                    causal_mask = torch.tril(torch.ones(fast_len, fast_len, dtype=torch.bool, device=device))
+                    att_2d_masks[:, query_start:query_end, key_start:key_end] = causal_mask[None, :, :]
+        
+        # Apply padding masks
+        pad_2d_masks = pad_masks[:, None, :] * pad_masks[:, :, None]
+        att_2d_masks = att_2d_masks & pad_2d_masks
+        
+        return att_2d_masks
+
+    def visualize_attention_mask(
+        self,
+        att_mask_segments,
+        att_2d_masks,
+        save_path,
+        batch_idx=0,
+        dpi=150,
+        max_display_tokens=None
+    ):
+        """Visualize the attention mask with labeled segments.
+        
+        Args:
+            att_mask_segments: List of (type, length) tuples defining the segments
+            att_2d_masks: 2D attention mask tensor [B, total_seq_len, total_seq_len]
+            save_path: Path where to save the visualization image
+            batch_idx: Which batch item to visualize (default: 0)
+            dpi: DPI for the saved image (default: 150)
+            max_display_tokens: Maximum number of tokens to display (for very long sequences)
+        """
+        try:
+            import matplotlib.pyplot as plt
+            import matplotlib.patches as mpatches
+            from matplotlib.colors import LinearSegmentedColormap
+        except ImportError:
+            logging.warning("matplotlib not available, skipping attention mask visualization")
+            return
+        
+        # Extract the mask for the specified batch
+        mask = att_2d_masks[batch_idx].cpu().float().numpy()
+        
+        # If sequence is too long, downsample for visualization
+        if max_display_tokens is not None and mask.shape[0] > max_display_tokens:
+            # Simple downsampling by taking every Nth token
+            step = mask.shape[0] // max_display_tokens
+            mask = mask[::step, ::step]
+            # Adjust segments accordingly
+            att_mask_segments = [(seg_type, max(1, seg_len // step)) for seg_type, seg_len in att_mask_segments]
+        
+        # Calculate positions for each segment
+        positions = []
+        current_pos = 0
+        for seg_type, seg_len in att_mask_segments:
+            positions.append((seg_type, current_pos, current_pos + seg_len))
+            current_pos += seg_len
+        
+        # Create figure
+        fig, ax = plt.subplots(figsize=(12, 10))
+        
+        # Create custom colormap: white for False (no attention), blue for True (attention)
+        colors = ['white', '#2E86AB']
+        n_bins = 2
+        cmap = LinearSegmentedColormap.from_list('attention', colors, N=n_bins)
+        
+        # Display the mask
+        im = ax.imshow(mask, cmap=cmap, aspect='auto', interpolation='nearest', vmin=0, vmax=1)
+        
+        # Add colorbar
+        cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
+        cbar.set_label('Attention Enabled', rotation=270, labelpad=20)
+        cbar.set_ticks([0.25, 0.75])
+        cbar.set_ticklabels(['No', 'Yes'])
+        
+        # Define colors for each segment type
+        segment_colors = {
+            'image': '#A23B72',
+            'language': '#F18F01',
+            'subtask': '#C73E1D',
+            'fast': '#6A994E'
+        }
+        
+        # Draw segment boundaries and labels
+        for seg_type, start, end in positions:
+            color = segment_colors.get(seg_type, '#666666')
+            
+            # Draw vertical lines for columns (keys)
+            ax.axvline(x=start - 0.5, color=color, linewidth=2, alpha=0.7)
+            ax.axvline(x=end - 0.5, color=color, linewidth=2, alpha=0.7)
+            
+            # Draw horizontal lines for rows (queries)
+            ax.axhline(y=start - 0.5, color=color, linewidth=2, alpha=0.7)
+            ax.axhline(y=end - 0.5, color=color, linewidth=2, alpha=0.7)
+            
+            # Add labels at the top
+            mid_pos = (start + end) / 2
+            ax.text(mid_pos, -mask.shape[0] * 0.02, f"{seg_type.upper()}\n({end - start})",
+                   ha='center', va='top', fontsize=10, fontweight='bold', color=color)
+            
+            # Add labels on the left
+            ax.text(-mask.shape[1] * 0.02, mid_pos, f"{seg_type.upper()}\n({end - start})",
+                   ha='right', va='center', fontsize=10, fontweight='bold', color=color, rotation=0)
+        
+        # Set axis labels
+        ax.set_xlabel('Key Position (tokens being attended to)', fontsize=12, fontweight='bold')
+        ax.set_ylabel('Query Position (tokens attending)', fontsize=12, fontweight='bold')
+        ax.set_title('Attention Mask Pattern\n(White = No Attention, Blue = Attention Allowed)', 
+                    fontsize=14, fontweight='bold', pad=20)
+        
+        # Create legend for segment types
+        legend_patches = []
+        attention_rules = {
+            'image': 'Bidirectional with lang',
+            'language': 'Bidirectional with images',
+            'subtask': 'Attends to img+lang, causal self',
+            'fast': 'Attends to all, causal self'
+        }
+        for seg_type, color in segment_colors.items():
+            if any(seg[0] == seg_type for seg in att_mask_segments):
+                rule = attention_rules.get(seg_type, '')
+                legend_patches.append(mpatches.Patch(color=color, label=f'{seg_type.upper()}: {rule}'))
+        
+        ax.legend(handles=legend_patches, loc='upper right', bbox_to_anchor=(1.15, 1.0),
+                 framealpha=0.9, fontsize=9)
+        
+        # Adjust layout and save
+        plt.tight_layout()
+        
+        # Ensure the directory exists
+        save_path = Path(save_path)
+        save_path.parent.mkdir(parents=True, exist_ok=True)
+        
+        plt.savefig(save_path, dpi=dpi, bbox_inches='tight')
+        plt.close()
+        
+        logging.info(f"Attention mask visualization saved to: {save_path}")
+
     def sample_noise(self, shape, device):
         return torch.normal(
             mean=0.0,
@@ -607,10 +807,18 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         )
         time = time_beta * self.config.time_sampling_scale + self.config.time_sampling_offset
         return time.to(dtype=torch.float32, device=device)
-
+    
     def embed_prefix(
-        self, images, img_masks, tokens, subtask_tokens, masks, subtask_masks
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, int]:
+        self, 
+        images, 
+        img_masks, 
+        tokens, 
+        subtask_tokens, 
+        masks, 
+        subtask_masks, 
+        fast_action_tokens=None, 
+        fast_action_masks=None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, int, int]:
         """Embed images with SigLIP, tokens, and optionally subtask tokens with embedding layer.
         
         Args:
@@ -619,17 +827,23 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             tokens: Language instruction tokens
             subtask_tokens: Subtask tokens to predict (can be None for inference)
             masks: Attention masks for tokens
+            fast_action_tokens: FAST action tokens for auxiliary prediction (can be None) - discrete token IDs
+            fast_action_masks: Padding masks for FAST action tokens (can be None)
             
         Returns:
-            embs: Concatenated embeddings [images, tokens, (subtask_tokens if provided)]
+            embs: Concatenated embeddings [images, tokens, (subtask_tokens if provided), (fast_action_tokens if provided)]
             pad_masks: Padding masks
-            att_masks: Attention masks (with causal masking for subtask prediction if subtask_tokens provided)
+            att_masks: Custom 2D attention mask implementing the required pattern
             total_T_images: Total number of image tokens
+            num_subtask_embs: Number of subtask token embeddings
+            num_fast_embs: Number of FAST action token embeddings
         """
         embs = []
         pad_masks = []
-        att_masks = []
+        att_mask_segments = []  # Store info about each segment for custom mask creation
         total_T_images = 0
+        num_subtask_embs = 0
+        num_fast_embs = 0
         
         # Process images
         for img, img_mask in zip(images, img_masks, strict=True):
@@ -642,7 +856,7 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
 
             embs.append(img_emb)
             pad_masks.append(img_mask[:, None].expand(bsize, num_img_embs))
-            att_masks += [0] * num_img_embs  # Images can attend to all previous tokens
+            att_mask_segments.append(('image', num_img_embs))
             total_T_images += num_img_embs
             
         # Process language instruction tokens
@@ -656,7 +870,7 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         pad_masks.append(masks)
 
         num_lang_embs = lang_emb.shape[1]
-        att_masks += [0] * num_lang_embs  # Language tokens can attend to all previous tokens (images + tokens)
+        att_mask_segments.append(('language', num_lang_embs))
 
         # Process subtask tokens if provided (these are predicted, so use causal masking)
         if subtask_tokens is not None:
@@ -672,18 +886,49 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             pad_masks.append(subtask_masks)
 
             num_subtask_embs = subtask_emb.shape[1]
-            # Causal masking for subtask tokens: each subtask token can attend to images, all instruction tokens,
-            # and previous subtask tokens
-            att_masks += [1] * num_subtask_embs  # Use 1 for causal attention on subtask tokens
+            att_mask_segments.append(('subtask', num_subtask_embs))
+        # Process FAST action tokens if provided (these are discrete token IDs)
+        if fast_action_tokens is not None:
+            def fast_action_embed_func(fast_action_tokens):
+                fast_emb = self.fast_action_embedding(fast_action_tokens)
+                fast_emb_dim = fast_emb.shape[-1]
+                return fast_emb * math.sqrt(fast_emb_dim)
+            
+            fast_action_emb = self._apply_checkpoint(fast_action_embed_func, fast_action_tokens)
+            embs.append(fast_action_emb)
+            
+            # Use provided mask or create default (all valid)
+            if fast_action_masks is not None:
+                fast_pad_mask = fast_action_masks
+            else:
+                bsize = fast_action_tokens.shape[0]
+                num_fast_embs = fast_action_tokens.shape[1]
+                fast_pad_mask = torch.ones(bsize, num_fast_embs, dtype=torch.bool, device=fast_action_tokens.device)
+            
+            num_fast_embs = fast_action_tokens.shape[1]
+            pad_masks.append(fast_pad_mask)
+            att_mask_segments.append(('fast', num_fast_embs))
 
         embs = torch.cat(embs, dim=1)
         pad_masks = torch.cat(pad_masks, dim=1)
-        att_masks = torch.tensor(att_masks, dtype=torch.bool, device=pad_masks.device)
+        
+        # Create custom 2D attention mask
+        # Attention rules:
+        # - Images + Language: bidirectional among themselves, don't attend to subtask or FAST
+        # - Subtask: attend to images + language, causal among themselves, don't attend to FAST
+        # - FAST: attend to images + language + subtask, causal among themselves
+        att_masks = self._create_custom_attention_mask(att_mask_segments, pad_masks, bsize)
 
-        bsize = pad_masks.shape[0]
-        att_masks = att_masks[None, :].expand(bsize, att_masks.shape[0])
+        # # Optionally visualize the attention mask
+        # self.visualize_attention_mask(
+        #     att_mask_segments=att_mask_segments,
+        #     att_2d_masks=att_masks,
+        #     save_path="/admin/home/jade_choghari/lerobot/src/lerobot/policies/pi05/attention_mask_visualization.png",
+        #     batch_idx=0,
+        #     max_display_tokens=512  # Limit display for very long sequences
+        # )
 
-        return embs, pad_masks, att_masks, total_T_images
+        return embs, pad_masks, att_masks, total_T_images, num_subtask_embs, num_fast_embs
 
     def embed_suffix(self, noisy_actions, timestep):
         """Embed noisy_actions, timestep to prepare for Expert Gemma processing."""
@@ -732,8 +977,8 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
 
         return embs, pad_masks, att_masks, adarms_cond
 
-    #  loss_dict = self.model.forward(images, img_masks, high_level_task, tokens, masks, subtask_tokens, subtask_masks, actions)
-    def forward(self, images, img_masks, high_level_task, high_level_task_masks, subtask_tokens, subtask_masks, actions, noise=None, time=None) -> Tensor:
+    #  loss_dict = self.model.forward(images, img_masks, high_level_task, tokens, masks, subtask_tokens, subtask_masks, actions, fast_action_tokens, fast_action_masks)
+    def forward(self, images, img_masks, high_level_task, high_level_task_masks, subtask_tokens, subtask_masks, actions, fast_action_tokens=None, fast_action_masks=None, noise=None, time=None) -> Tensor:
         """Do a full training forward pass and compute the loss.
         
         Args:
@@ -743,7 +988,9 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             high_level_task_masks: Attention masks for high_level_task
             subtask_tokens: Subtask tokens to predict (e.g., tokens for "pick up the cup")
             subtask_masks: Attention masks for subtask_tokens
-            actions: Ground truth actions
+            actions: Ground truth actions [B, chunk_size, action_dim]
+            fast_action_tokens: Discrete action token IDs [B, max_action_tokens]
+            fast_action_masks: Padding masks for fast action tokens [B, max_action_tokens]
             noise: Optional noise for flow matching
             time: Optional time for flow matching
         """
@@ -756,77 +1003,184 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         time_expanded = time[:, None, None]
         x_t = time_expanded * noise + (1 - time_expanded) * actions
         u_t = noise - actions
-        
-        # Embed prefix (images + high_level_task + subtask_tokens)
-        # Use high_level_task (prompt WITHOUT subtask) + subtask_tokens to predict
-        prefix_embs, prefix_pad_masks, prefix_att_masks, total_T_images = self.embed_prefix(
-            images, img_masks, high_level_task, subtask_tokens, high_level_task_masks, subtask_masks
+
+        # Initialize FAST loss to 0 (will be computed only if FAST tokens are provided)
+        fast_loss = torch.tensor(0.0, device=actions.device, dtype=actions.dtype)
+
+        # ========== PASS 1: Prefix with FAST tokens for subtask + FAST prediction ==========
+        # Only run this pass if FAST action tokens are provided
+        if fast_action_tokens is not None and fast_action_masks is not None:
+            # Embed prefix (images + high_level_task + subtask_tokens + FAST tokens)
+            # FAST tokens are provided as discrete token IDs
+            prefix_with_fast_embs, prefix_with_fast_pad_masks, prefix_with_fast_att_masks, total_T_images, num_subtask_embs, num_fast_embs = self.embed_prefix(
+                images, img_masks, high_level_task, subtask_tokens, high_level_task_masks, subtask_masks, 
+                fast_action_tokens=fast_action_tokens, fast_action_masks=fast_action_masks
+            )
+
+            # Convert embeddings to bfloat16 if needed for the model
+            if (
+                self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
+                == torch.bfloat16
+            ):
+                prefix_with_fast_embs = prefix_with_fast_embs.to(dtype=torch.bfloat16)
+
+            # Prepare attention masks for prefix pass with FAST tokens
+            position_ids_prefix_with_fast = torch.cumsum(prefix_with_fast_pad_masks, dim=1) - 1
+            att_2d_prefix_with_fast_4d = self._prepare_attention_masks_4d(prefix_with_fast_att_masks, dtype=prefix_with_fast_embs.dtype)
+
+            # Forward pass through paligemma for subtask + FAST prediction
+            (prefix_with_fast_out, _), _ = self.paligemma_with_expert.forward(
+                attention_mask=att_2d_prefix_with_fast_4d,
+                position_ids=position_ids_prefix_with_fast,
+                past_key_values=None,
+                inputs_embeds=[prefix_with_fast_embs, None],  # SUFFIX = None
+                use_cache=False,
+                adarms_cond=[None, None],
+            )
+
+            # LM HEAD → SUBTASK LOGITS
+            lm_head = self.paligemma_with_expert.paligemma.lm_head
+            logits = lm_head(prefix_with_fast_out)  # (B, T_prefix_with_fast, vocab)
+
+            # Extract logits for subtask token prediction
+            T_high_level_task = high_level_task.size(1)
+            T_subtask = subtask_tokens.size(1)
+            start_index = total_T_images + T_high_level_task
+            end_index = start_index + T_subtask
+            logits_subtask = logits[:, start_index-1:end_index-1, :]  # (B, T_subtask, vocab)
+
+            targets = subtask_tokens  # (B, T_subtask)
+            # Compute cross-entropy loss for subtask
+            loss_fct = torch.nn.CrossEntropyLoss(reduction='none')
+            logits_flat = logits_subtask.reshape(-1, logits_subtask.size(-1))
+            targets_flat = targets.reshape(-1)
+            loss_per_token = loss_fct(logits_flat, targets_flat)
+            loss_per_token = loss_per_token.reshape(targets.shape)
+            masked_loss = loss_per_token * subtask_masks.float()
+            subtask_loss = masked_loss.sum() / subtask_masks.sum().clamp(min=1)
+
+            # Extract outputs for FAST action token prediction and compute auxiliary loss
+            # FAST outputs start after subtask tokens
+            # Similar to subtask, we use autoregressive prediction where position i predicts token i+1
+            fast_start_index = end_index
+            fast_end_index = fast_start_index + num_fast_embs
+            
+            # Get logits for FAST action tokens using the FAST LM head
+            fast_logits = self.fast_action_lm_head(prefix_with_fast_out)  # (B, T_prefix_with_fast, fast_vocab_size)
+            
+            # Extract logits for FAST token prediction (autoregressive: position i predicts token i+1)
+            # - Position (fast_start_index-1) predicts fast_action_tokens[0]
+            # - Position (fast_start_index) predicts fast_action_tokens[1], etc.
+            fast_logits_for_pred = fast_logits[:, fast_start_index-1:fast_end_index-1, :]  # (B, max_action_tokens, fast_vocab_size)
+            
+            # Compute cross-entropy loss for FAST action tokens
+            fast_targets = fast_action_tokens  # (B, max_action_tokens)
+            loss_fct_fast = torch.nn.CrossEntropyLoss(reduction='none')
+            fast_logits_flat = fast_logits_for_pred.reshape(-1, fast_logits_for_pred.size(-1))  # (B*max_action_tokens, fast_vocab_size)
+            fast_targets_flat = fast_targets.reshape(-1)  # (B*max_action_tokens)
+            
+            fast_loss_per_token = loss_fct_fast(fast_logits_flat, fast_targets_flat)  # (B*max_action_tokens)
+            fast_loss_per_token = fast_loss_per_token.reshape(fast_targets.shape)  # (B, max_action_tokens)
+            
+            # Apply mask and compute mean loss over valid tokens
+            masked_fast_loss = fast_loss_per_token * fast_action_masks.float()
+            fast_loss = masked_fast_loss.sum() / fast_action_masks.sum().clamp(min=1)
+        else:
+            # If no FAST tokens provided, compute subtask loss without FAST tokens
+            # This is the fallback for backward compatibility
+            prefix_embs_for_subtask, prefix_pad_masks_for_subtask, prefix_att_masks_for_subtask, total_T_images, _, _ = self.embed_prefix(
+                images, img_masks, high_level_task, subtask_tokens, high_level_task_masks, subtask_masks,
+                fast_action_tokens=None, fast_action_masks=None
+            )
+            
+            # Convert embeddings to bfloat16 if needed for the model
+            if (
+                self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
+                == torch.bfloat16
+            ):
+                prefix_embs_for_subtask = prefix_embs_for_subtask.to(dtype=torch.bfloat16)
+            
+            position_ids_prefix = torch.cumsum(prefix_pad_masks_for_subtask, dim=1) - 1
+            att_2d_prefix_4d = self._prepare_attention_masks_4d(prefix_att_masks_for_subtask, dtype=prefix_embs_for_subtask.dtype)
+            
+            (prefix_out, _), _ = self.paligemma_with_expert.forward(
+                attention_mask=att_2d_prefix_4d,
+                position_ids=position_ids_prefix,
+                past_key_values=None,
+                inputs_embeds=[prefix_embs_for_subtask, None],
+                use_cache=False,
+                adarms_cond=[None, None],
+            )
+            
+            lm_head = self.paligemma_with_expert.paligemma.lm_head
+            logits = lm_head(prefix_out)
+            
+            T_high_level_task = high_level_task.size(1)
+            T_subtask = subtask_tokens.size(1)
+            start_index = total_T_images + T_high_level_task
+            end_index = start_index + T_subtask
+            logits_subtask = logits[:, start_index-1:end_index-1, :]
+            
+            targets = subtask_tokens
+            loss_fct = torch.nn.CrossEntropyLoss(reduction='none')
+            logits_flat = logits_subtask.reshape(-1, logits_subtask.size(-1))
+            targets_flat = targets.reshape(-1)
+            loss_per_token = loss_fct(logits_flat, targets_flat)
+            loss_per_token = loss_per_token.reshape(targets.shape)
+            masked_loss = loss_per_token * subtask_masks.float()
+            subtask_loss = masked_loss.sum() / subtask_masks.sum().clamp(min=1)
+
+        # ========== PASS 2: Full forward WITHOUT FAST tokens for flow matching ==========
+        # Embed prefix WITHOUT FAST tokens (images + high_level_task + subtask_tokens)
+        prefix_embs_no_fast, prefix_pad_masks_no_fast, prefix_att_masks_no_fast, _, _, _ = self.embed_prefix(
+            images, img_masks, high_level_task, subtask_tokens, high_level_task_masks, subtask_masks, 
+            fast_action_tokens=None, fast_action_masks=None
         )
         
         suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(x_t, time)
 
-        # Prepare attention masks for prefix-only pass (for subtask token prediction)
-        att_2d_prefix = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
-        position_ids_prefix = torch.cumsum(prefix_pad_masks, dim=1) - 1
-        att_2d_prefix_4d = self._prepare_attention_masks_4d(att_2d_prefix, dtype=prefix_embs.dtype)
-
-        # prefix-only transformer run for subtask token prediction
-        (prefix_out, _), _ = self.paligemma_with_expert.forward(
-            attention_mask=att_2d_prefix_4d,
-            position_ids=position_ids_prefix,
-            past_key_values=None,
-            inputs_embeds=[prefix_embs, None],  # SUFFIX = None
-            use_cache=False,
-            adarms_cond=[None, None],
-        )
-
-        # LM HEAD → SUBTASK LOGITS
-        # prefix_out: (B, T_prefix, H) where T_prefix = total_T_images + T_high_level_task + T_subtask
-        lm_head = self.paligemma_with_expert.paligemma.lm_head
-        logits = lm_head(prefix_out)  # (B, T_prefix, vocab)
-
-        # Extract logits for subtask token prediction
-        # In autoregressive modeling, output at position i predicts token at position i+1
-        # So we take logits from one position earlier:
-        # - Position (start_index-1) (last high_level_task token) predicts subtask_tokens[0]
-        # - Position (start_index) (first subtask token) predicts subtask_tokens[1], etc.
-        T_high_level_task = high_level_task.size(1)
-        T_subtask = subtask_tokens.size(1)
-        start_index = total_T_images + T_high_level_task
-        end_index = start_index + T_subtask
-        logits_subtask = logits[:, start_index-1:end_index-1, :]  # (B, T_subtask, vocab)
-
-        targets = subtask_tokens  # (B, T_subtask)
-        # Compute cross-entropy loss
-        loss_fct = torch.nn.CrossEntropyLoss(reduction='none')
-        # Reshape for loss computation
-        logits_flat = logits_subtask.reshape(-1, logits_subtask.size(-1))  # (B*T_subtask, vocab)
-        targets_flat = targets.reshape(-1)  # (B*T_subtask)
-
-        loss_per_token = loss_fct(logits_flat, targets_flat)  # (B*T_subtask)
-        loss_per_token = loss_per_token.reshape(targets.shape)  # (B, T_subtask)
-
-        # Apply mask and compute mean loss over valid tokens
-        masked_loss = loss_per_token * subtask_masks.float()
-        subtask_loss = masked_loss.sum() / subtask_masks.sum().clamp(min=1)
-        
-        breakpoint()
         # Convert embeddings to bfloat16 if needed for the model
         if (
             self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
             == torch.bfloat16
         ):
             suffix_embs = suffix_embs.to(dtype=torch.bfloat16)
-            prefix_embs = prefix_embs.to(dtype=torch.bfloat16)
+            prefix_embs_no_fast = prefix_embs_no_fast.to(dtype=torch.bfloat16)
 
-        # Concatenate prefix (images + tokens + subtask_tokens) and suffix (actions) masks
-        pad_masks = torch.cat([prefix_pad_masks, suffix_pad_masks], dim=1)
-        att_masks = torch.cat([prefix_att_masks, suffix_att_masks], dim=1)
-
-        # Prepare attention masks for full forward pass (prefix + suffix)
-        att_2d_masks = make_att_2d_masks(pad_masks, att_masks)
+        # For the flow matching pass, we need custom attention where:
+        # - prefix follows the custom pattern (images+lang bidirectional, subtask causal, no cross-attention)
+        # - suffix attends to all prefix + causal to itself
+        # We'll construct this by extending prefix_att_masks_no_fast to include suffix
+        
+        # prefix_att_masks_no_fast is already a 2D boolean mask [B, prefix_len, prefix_len]
+        # We need to extend it to [B, prefix_len + suffix_len, prefix_len + suffix_len]
+        
+        bsize = prefix_pad_masks_no_fast.shape[0]
+        prefix_len = prefix_pad_masks_no_fast.shape[1]
+        suffix_len = suffix_pad_masks.shape[1]
+        total_len = prefix_len + suffix_len
+        device = prefix_pad_masks_no_fast.device
+        
+        # Create full attention mask
+        full_att_2d_masks = torch.zeros(bsize, total_len, total_len, dtype=torch.bool, device=device)
+        
+        # Copy prefix attention pattern
+        full_att_2d_masks[:, :prefix_len, :prefix_len] = prefix_att_masks_no_fast
+        
+        # Suffix attends to all prefix
+        full_att_2d_masks[:, prefix_len:, :prefix_len] = True
+        
+        # Suffix has causal attention among itself
+        suffix_causal_mask = torch.tril(torch.ones(suffix_len, suffix_len, dtype=torch.bool, device=device))
+        full_att_2d_masks[:, prefix_len:, prefix_len:] = suffix_causal_mask[None, :, :]
+        
+        # Apply padding masks
+        pad_masks = torch.cat([prefix_pad_masks_no_fast, suffix_pad_masks], dim=1)
+        pad_2d_masks = pad_masks[:, None, :] * pad_masks[:, :, None]
+        full_att_2d_masks = full_att_2d_masks & pad_2d_masks
+        
         position_ids = torch.cumsum(pad_masks, dim=1) - 1
-        att_2d_masks_4d = self._prepare_attention_masks_4d(att_2d_masks, dtype=prefix_embs.dtype)
+        att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks, dtype=prefix_embs_no_fast.dtype)
 
         def forward_func(prefix_embs, suffix_embs, att_2d_masks_4d, position_ids, adarms_cond):
             (_, suffix_out), _ = self.paligemma_with_expert.forward(
@@ -837,11 +1191,10 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
                 use_cache=False,
                 adarms_cond=[None, adarms_cond],
             )
-            # prefix_out to be used for the language head
             return suffix_out
 
         suffix_out = self._apply_checkpoint(
-            forward_func, prefix_embs, suffix_embs, att_2d_masks_4d, position_ids, adarms_cond
+            forward_func, prefix_embs_no_fast, suffix_embs, att_2d_masks_4d, position_ids, adarms_cond
         )
 
         suffix_out = suffix_out[:, -self.config.chunk_size :]
@@ -857,80 +1210,81 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         return {
             "flow_loss": fm_loss,
             "subtask_loss": subtask_loss,
-            "loss": 10 * fm_loss.mean() + subtask_loss,
+            "fast_loss": fast_loss,
+            "loss": fm_loss.mean() + 0.1 * subtask_loss + 0.05 * fast_loss, # ref: b1k winner
         }
-
+    
     @torch.no_grad()
     def _generate_subtask_tokens(
         self, images, img_masks, tokens, masks, tokenizer, max_length, device
     ):
-        """Generate subtask tokens autoregressively using next token prediction."""
         bsize = tokens.shape[0]
-        
-        # Get lm_head for token generation
         lm_head = self.paligemma_with_expert.paligemma.lm_head
         
-        # Embed prefix without subtask tokens first
-        prefix_embs, prefix_pad_masks, prefix_att_masks, total_T_images = self.embed_prefix(
-            images, img_masks, tokens, subtask_tokens=None, masks=masks, subtask_masks=None
+        prefix_embs, prefix_pad_masks, prefix_att_masks, total_T_images, _, _ = self.embed_prefix(
+            images, img_masks, tokens, subtask_tokens=None, masks=masks, subtask_masks=None, 
+            fast_action_tokens=None, fast_action_masks=None
         )
-        
-        # Initialize generated tokens list - start with BOS token or first token after instruction
-        # For PaliGemma, we'll start generation and accumulate tokens
+
         generated_tokens = torch.zeros((bsize, max_length), dtype=torch.long, device=device)
         
+        # tracking mask: False = still generating, True = finished
+        finished = torch.zeros(bsize, dtype=torch.bool, device=device)
+        
         for t in range(max_length):
-            # Prepare attention masks for current prefix
-            att_2d_prefix = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
             position_ids_prefix = torch.cumsum(prefix_pad_masks, dim=1) - 1
-            att_2d_prefix_4d = self._prepare_attention_masks_4d(att_2d_prefix, dtype=prefix_embs.dtype)
-            
-            # Forward pass through model to get logits
+            att_2d_prefix_4d = self._prepare_attention_masks_4d(prefix_att_masks, dtype=prefix_embs.dtype)
+
             (prefix_out, _), _ = self.paligemma_with_expert.forward(
-                attention_mask=att_2d_prefix_4d,
+                attention_mask=att_2d_prefix_4d, 
                 position_ids=position_ids_prefix,
-                past_key_values=None,
                 inputs_embeds=[prefix_embs, None],
-                use_cache=False,
-                adarms_cond=[None, None],
+                # ...
             )
             
-            # Get logits from the last position
-            logits = lm_head(prefix_out)  # (B, T_prefix, vocab)
-            next_token_logits = logits[:, -1, :]  # (B, vocab)
+            logits = lm_head(prefix_out)
+            next_token_logits = logits[:, -1, :]
+            next_token = torch.argmax(next_token_logits, dim=-1) # (B,)
             
-            # Greedy decoding - take the most likely token
-            next_token = torch.argmax(next_token_logits, dim=-1)  # (B,)
+            # 1. if a row was already finished, force the next token to be PAD (0)
+            next_token = torch.where(finished, torch.tensor(0, device=device), next_token)
             
-            # Store generated token
+            # 2. store the token
             generated_tokens[:, t] = next_token
             
-            # Check for EOS token - if all batches have generated EOS, stop
+            # 3. update the finished mask
             if tokenizer.eos_token_id is not None:
-                if (next_token == tokenizer.eos_token_id).all():
-                    break
+                finished |= (next_token == tokenizer.eos_token_id)
             
-            # Embed the generated token and append to prefix
-            next_token_unsqueezed = next_token.unsqueeze(1)  # (B, 1)
-            breakpoint()
+            # 4. break only if everyone is finished
+            if finished.all():
+                break
+                
+            next_token_unsqueezed = next_token.unsqueeze(1)
             
             def next_token_embed_func(next_token_unsqueezed):
                 next_emb = self.paligemma_with_expert.embed_language_tokens(next_token_unsqueezed)
-                next_emb_dim = next_emb.shape[-1]
-                return next_emb * math.sqrt(next_emb_dim)
+                return next_emb * math.sqrt(next_emb.shape[-1])
             
             next_emb = self._apply_checkpoint(next_token_embed_func, next_token_unsqueezed)
             
-            # Append to prefix embeddings
+            # update embeddings
             prefix_embs = torch.cat([prefix_embs, next_emb], dim=1)
             
-            # Update masks - new token is valid and uses causal attention
+            # update padding masks
             prefix_pad_masks = torch.cat([
                 prefix_pad_masks,
                 torch.ones((bsize, 1), dtype=torch.bool, device=device)
             ], dim=1)
-            prefix_att_masks = torch.cat([prefix_att_masks, torch.ones((bsize, 1), dtype=torch.bool, device=device)], dim=1)
-        
+            
+            # update attention masks
+            old_seq_len = prefix_att_masks.shape[1]
+            new_seq_len = old_seq_len + 1
+            new_att_masks = torch.zeros((bsize, new_seq_len, new_seq_len), dtype=torch.bool, device=device)
+            new_att_masks[:, :old_seq_len, :old_seq_len] = prefix_att_masks
+            new_att_masks[:, -1, :] = prefix_pad_masks
+            prefix_att_masks = new_att_masks
+
         return generated_tokens
 
     @torch.no_grad()  # see openpi `sample_actions` (slightly adapted)
@@ -980,13 +1334,23 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         subtask_masks = torch.ones_like(generated_subtask_tokens, dtype=torch.bool)
 
         # During inference, we don't have subtask_tokens yet, so pass None
-        prefix_embs, prefix_pad_masks, prefix_att_masks, _ = self.embed_prefix(
-            images, img_masks, tokens, subtask_tokens=generated_subtask_tokens, masks=masks, subtask_masks=subtask_masks
+        # Also no FAST tokens during inference
+        prefix_embs, prefix_pad_masks, prefix_att_masks, _, _, _ = self.embed_prefix(
+            images, img_masks, tokens, subtask_tokens=generated_subtask_tokens, masks=masks, subtask_masks=subtask_masks, 
+            fast_action_tokens=None, fast_action_masks=None
         )
-        prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
+        
+        # Convert embeddings to bfloat16 if needed for the model
+        if (
+            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
+            == torch.bfloat16
+        ):
+            prefix_embs = prefix_embs.to(dtype=torch.bfloat16)
+        
+        # prefix_att_masks is already a 2D attention mask from embed_prefix
         prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
 
-        prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d_masks, dtype=prefix_embs.dtype)
+        prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_masks, dtype=prefix_embs.dtype)
         self.paligemma_with_expert.paligemma.language_model.config._attn_implementation = "eager"  # noqa: SLF001
 
         _, past_key_values = self.paligemma_with_expert.forward(
@@ -1211,7 +1575,7 @@ class PI05Policy(PreTrainedPolicy):
                 print(f"Remapped {remap_count} state dict keys")
 
             # Load the remapped state dict into the model
-            missing_keys, unexpected_keys = model.load_state_dict(remapped_state_dict, strict=strict)
+            missing_keys, unexpected_keys = model.load_state_dict(remapped_state_dict, strict=False)
 
             if missing_keys:
                 print(f"Missing keys when loading state dict: {len(missing_keys)} keys")
@@ -1419,7 +1783,7 @@ class PI05Policy(PreTrainedPolicy):
         # Use high_level_task tokens (WITHOUT subtask) for inference - we'll generate the subtask
         high_level_task = batch[f"{OBS_LANGUAGE_HIGH_LEVEL_TASK_TOKENS}"]
         high_level_task_masks = batch[f"{OBS_LANGUAGE_HIGH_LEVEL_TASK_ATTENTION_MASK}"]
-        breakpoint()
+        
         # Sample actions using the model (pass through RTC kwargs, no separate state needed for PI05)
         actions = self.model.sample_actions(
             images, img_masks, high_level_task, high_level_task_masks, 
@@ -1444,29 +1808,36 @@ class PI05Policy(PreTrainedPolicy):
         actions = self.prepare_action(batch)
         
         # Decode and print ground truth subtask tokens during training
-        if self.tokenizer is not None and self.training:
-            bsize = subtask_tokens.shape[0]
-            for i in range(bsize):
-                # Remove padding tokens (0) and special tokens
-                valid_tokens = subtask_tokens[i][subtask_masks[i].bool()]
-                if len(valid_tokens) > 0:
-                    decoded_text = self.tokenizer.decode(valid_tokens, skip_special_tokens=True)
-                    print(f"[Training] Ground truth subtask {i}: {decoded_text}")
+        # if self.tokenizer is not None and self.training:
+        #     bsize = subtask_tokens.shape[0]
+        #     for i in range(bsize):
+        #         # Remove padding tokens (0) and special tokens
+        #         valid_tokens = subtask_tokens[i][subtask_masks[i].bool()]
+        #         # if len(valid_tokens) > 0:
+        #             # decoded_text = self.tokenizer.decode(valid_tokens, skip_special_tokens=True)
+        #             # print(f"[Training] Ground truth subtask {i}: {decoded_text}")
         
+        # Get FAST action tokens from batch
+        fast_action_tokens = batch.get("action.tokens", None)  # (B, max_action_tokens)
+        fast_action_masks = batch.get("action.token_mask", None)  # (B, max_action_tokens)
         # Compute loss (no separate state needed for PI05)
         # high_level_task = instruction tokens WITHOUT subtask (e.g., "High level task: X; State: Y; Subtask:")
         # subtask_tokens = subtask tokens to predict (e.g., "pick up the cup")
-        loss_dict = self.model.forward(images, img_masks, high_level_task, high_level_task_masks, subtask_tokens, subtask_masks, actions)
+        # fast_action_tokens = discrete action token IDs to predict
+        loss_dict = self.model.forward(
+            images, img_masks, high_level_task, high_level_task_masks, subtask_tokens, subtask_masks, actions,
+            fast_action_tokens=fast_action_tokens, fast_action_masks=fast_action_masks
+        )
 
         # Extract the total loss
         loss = loss_dict["loss"]
         
-        breakpoint()
         # Prepare detailed loss dictionary for logging
         detailed_loss_dict = {
             "loss": loss.item(),
             "flow_loss": loss_dict["flow_loss"].mean().item(),
             "subtask_loss": loss_dict["subtask_loss"].item(),
+            "fast_loss": loss_dict["fast_loss"].item(),
         }
 
         return loss, detailed_loss_dict

src/lerobot/policies/pi05/processor_pi05.py

@@ -33,6 +33,7 @@ from lerobot.processor import (
     ProcessorStep,
     ProcessorStepRegistry,
     RenameObservationsProcessorStep,
+    ActionTokenizerProcessorStep,
     TokenizerProcessorStep,
     UnnormalizerProcessorStep,
 )
@@ -158,7 +159,6 @@ def make_pi05_pre_post_processors(
     Returns:
         A tuple containing the configured pre-processor and post-processor pipelines.
     """
-
     # Add remaining processors
     input_steps: list[ProcessorStep] = [
         RenameObservationsProcessorStep(rename_map={}),  # To mimic the same processor as pretrained one
@@ -177,6 +177,9 @@ def make_pi05_pre_post_processors(
             padding_side="right",
             padding="max_length",
         ),
+        ActionTokenizerProcessorStep(
+            tokenizer_name="/fsx/jade_choghari/outputs/fast_tokenizer", # TODO: jade put the PI
+        ),
         DeviceProcessorStep(device=config.device),
     ]
 
@@ -186,7 +189,7 @@ def make_pi05_pre_post_processors(
         ),
         DeviceProcessorStep(device="cpu"),
     ]
-
+    
     return (
         PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
             steps=input_steps,

src/lerobot/policies/pi05/train.sh

@@ -0,0 +1,22 @@
+export CUDA_LAUNCH_BLOCKING=1 
+lerobot-train \
+    --dataset.repo_id=local \
+    --dataset.root=/fsx/jade_choghari/outputs/collect-data-pgen \
+    --output_dir=/fsx/jade_choghari/outputs/pi0_fast_fruit1 \
+    --job_name=pi0_training \
+    --policy.repo_id=jade_choghari/pi0-base1 \
+    --policy.path=lerobot/pi05_base \
+    --policy.dtype=bfloat16 \
+    --steps=200000 \
+    --save_freq=5000 \
+    --rename_map='{
+        "observation.images.base": "observation.images.base_0_rgb",
+        "observation.images.left_wrist": "observation.images.left_wrist_0_rgb",
+        "observation.images.right_wrist": "observation.images.right_wrist_0_rgb",
+        }' \
+    --batch_size=4 \
+    --policy.device=cuda \
+    --wandb.enable=true \
+    --wandb.disable_artifact=true \
+    --wandb.project=pi05hi-training \
+# /fsx/jade_choghari/.cache/huggingface/lerobot/jadechoghari/collect-data

src/lerobot/policies/pi05/train2.sh

@@ -0,0 +1,18 @@
+rm -rf /fsx/jade_choghari/outputs/pi0_multi_training
+lerobot-train \
+    --dataset.repo_id=local\
+    --dataset.root=/fsx/jade_choghari/outputs/collect-data-pgen \
+    --output_dir=/fsx/jade_choghari/outputs/pi0_multi_training \
+    --job_name=pi0_multi_training \
+    --policy.repo_id=jadechoghari/pi0-base1 \
+    --policy.path=lerobot/pi05_base \
+    --policy.dtype=bfloat16 \
+    --steps=50000 \
+    --save_freq=5000 \
+    --rename_map='{
+        "observation.images.base": "observation.images.base_0_rgb",
+        "observation.images.left_wrist": "observation.images.left_wrist_0_rgb",
+        "observation.images.right_wrist": "observation.images.right_wrist_0_rgb",
+        }' \
+    --batch_size=32 \
+    --policy.device=cuda \

src/lerobot/policies/pi05/train_fast.sh

@@ -0,0 +1,9 @@
+python src/lerobot/policies/pi05/train_fast_tokenizer.py \
+    --repo_id "local" \
+    --root "/fsx/jade_choghari/outputs/collect-data-pgen" \
+    --action_horizon 16 \
+    --encoded_dims "0:15" \
+    --action_horizon 50 \
+    --vocab_size 1024 \
+    --scale 10.0 \
+    --output_dir "/fsx/jade_choghari/outputs/fast_tokenizer"

src/lerobot/policies/pi05/train_fast_tokenizer.py

@@ -0,0 +1,410 @@
+"""Train FAST tokenizer for action encoding.
+
+This script:
+1. Loads action chunks from LeRobotDataset (with sampling)
+2. Applies delta transforms and per-timestamp normalization
+3. Trains FAST tokenizer on specified action dimensions
+4. Saves tokenizer to assets directory
+5. Reports compression statistics
+"""
+
+import json
+import numpy as np
+import tyro
+from pathlib import Path
+from transformers import AutoProcessor
+import torch
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+
+def apply_delta_transform(state: np.ndarray, actions: np.ndarray, delta_dims: list[int] | None) -> np.ndarray:
+    """Apply delta transform to specified dimensions.
+    
+    Args:
+        state: Current state [D]
+        actions: Future actions [D]
+        delta_dims: List of dimension indices to apply delta transform to
+    
+    Returns:
+        Transformed actions [D]
+    """
+    if delta_dims is None or len(delta_dims) == 0:
+        return actions
+    
+    delta_actions = actions.copy()
+    for dim in delta_dims:
+        delta_actions[dim] = actions[dim] - state[dim]
+    
+    return delta_actions
+
+
+def process_episode(args):
+    """Process single episode and return action chunks."""
+    dataset, ep_idx, action_horizon, delta_dims, sample_fraction, state_key, use_delta_transform = args
+    
+    try:
+        # Get episode info
+        ep_info = dataset.meta.episodes[ep_idx]
+        from_idx = ep_info["dataset_from_index"]
+        to_idx = ep_info["dataset_to_index"]
+        ep_length = to_idx - from_idx
+        
+        if ep_length < action_horizon:
+            return None
+        
+        # Load all frames in episode
+        # If dataset has episode filtering, we need to use the mapping
+        states = []
+        actions = []
+        
+        for abs_idx in range(from_idx, to_idx):
+            # Map absolute index to relative index if needed
+            if dataset._absolute_to_relative_idx is not None:
+                if abs_idx not in dataset._absolute_to_relative_idx:
+                    # This episode's frames aren't in the filtered dataset
+                    return None
+                rel_idx = dataset._absolute_to_relative_idx[abs_idx]
+            else:
+                rel_idx = abs_idx
+            
+            frame = dataset.hf_dataset[rel_idx]
+            
+            # Get state (could be from observation.state or other state key)
+            if state_key in frame:
+                state = frame[state_key].numpy() if torch.is_tensor(frame[state_key]) else np.array(frame[state_key])
+            else:
+                # If no state key, use zeros (no delta transform)
+                state = np.zeros_like(frame["action"].numpy() if torch.is_tensor(frame["action"]) else np.array(frame["action"]))
+            
+            action = frame["action"].numpy() if torch.is_tensor(frame["action"]) else np.array(frame["action"])
+            
+            states.append(state)
+            actions.append(action)
+        
+        states = np.array(states)
+        actions = np.array(actions)
+        
+        # Create action chunks (sliding window)
+        # All actions in a chunk are relative to the FIRST state in that chunk
+        action_chunks = []
+        
+        for i in range(len(states) - action_horizon + 1):
+            current_state = states[i]  # First state in chunk
+            future_absolute_actions = actions[i:i + action_horizon]
+            
+            if use_delta_transform:
+                # Relative actions
+                delta_chunk = np.zeros_like(future_absolute_actions)
+                for t in range(action_horizon):
+                    delta_chunk[t] = apply_delta_transform(
+                        current_state,
+                        future_absolute_actions[t],
+                        delta_dims,
+                    )
+                action_chunks.append(delta_chunk)
+            else:
+                # Absolute actions (NO delta)
+                action_chunks.append(future_absolute_actions)
+        
+        if len(action_chunks) == 0:
+            return None
+        
+        action_chunks = np.array(action_chunks)
+        
+        # Sample chunks
+        if sample_fraction < 1.0:
+            n_chunks = len(action_chunks)
+            n_samples = max(1, int(n_chunks * sample_fraction))
+            episode_seed = hash(ep_idx) % (2**31)
+            rng = np.random.RandomState(episode_seed)
+            indices = rng.choice(n_chunks, size=n_samples, replace=False)
+            action_chunks = action_chunks[indices]
+        
+        return action_chunks
+        
+    except Exception as e:
+        print(f"Error processing episode {ep_idx}: {e}")
+        import traceback
+        traceback.print_exc()
+        return None
+
+
+def train_fast_tokenizer(
+    action_chunks: np.ndarray,
+    vocab_size: int = 1024,
+    scale: float = 10.0,
+) -> AutoProcessor:
+    """
+    Train FAST tokenizer (BPE on DCT coefficients) on action chunks.
+    
+    Uses the .fit() method to train a new tokenizer on the provided data.
+    
+    Args:
+        action_chunks: Array of action chunks [N, H, D] where N=num_chunks, H=horizon, D=action_dim
+        vocab_size: BPE vocabulary size
+        scale: DCT scaling factor for quantization
+    
+    Returns:
+        Trained FAST tokenizer
+    """
+    print(f"Training FAST tokenizer on {len(action_chunks)} action chunks...")
+    print(f"Action chunk shape: {action_chunks.shape}")
+    print(f"Vocab size: {vocab_size}")
+    print(f"DCT scale: {scale}")
+    
+    # Download the tokenizer source code (not pretrained weights)
+    # We'll train a new tokenizer on our own data
+    base_tokenizer = AutoProcessor.from_pretrained(
+        "physical-intelligence/fast",
+        trust_remote_code=True
+    )
+    
+    # Convert action_chunks array to list of arrays (expected by .fit())
+    action_data_list = [action_chunks[i] for i in range(len(action_chunks))]
+    
+    # Train the new tokenizer on our action data using .fit()
+    # This trains the BPE tokenizer on DCT coefficients
+    print("Training new tokenizer (this may take a few minutes)...")
+    tokenizer = base_tokenizer.fit(
+        action_data_list,
+        scale=scale,
+        vocab_size=vocab_size,
+        time_horizon=action_chunks.shape[1],  # action_horizon
+        action_dim=action_chunks.shape[2],     # encoded dimensions
+    )
+    print("✓ Tokenizer training complete!")
+    
+    # Validate it works
+    sample_chunk = action_chunks[0]
+    encoded = tokenizer(sample_chunk[None])[0]
+    if isinstance(encoded, list):
+        encoded = np.array(encoded)
+    print(f"Sample encoding: {len(encoded)} tokens for chunk shape {sample_chunk.shape}")
+    
+    return tokenizer
+
+
+def compute_compression_stats(tokenizer, action_chunks: np.ndarray):
+    """Compute compression statistics."""
+    print("\nComputing compression statistics...")
+    
+    # Sample for stats (use max 1000 chunks for speed)
+    sample_size = min(1000, len(action_chunks))
+    sample_indices = np.random.RandomState(42).choice(len(action_chunks), size=sample_size, replace=False)
+    sample_chunks = action_chunks[sample_indices]
+    
+    token_lengths = []
+    for chunk in sample_chunks:
+        encoded = tokenizer(chunk[None])[0]
+        if isinstance(encoded, list):
+            token_lengths.append(len(encoded))
+        else:
+            token_lengths.append(encoded.shape[0] if hasattr(encoded, 'shape') else len(encoded))
+    
+    token_lengths = np.array(token_lengths)
+    
+    # Compression ratio: (H * D) / avg_tokens
+    input_size = action_chunks.shape[1] * action_chunks.shape[2]
+    avg_tokens = np.mean(token_lengths)
+    compression_ratio = input_size / avg_tokens
+    
+    stats = {
+        'compression_ratio': float(compression_ratio),
+        'mean_token_length': float(np.mean(token_lengths)),
+        'p99_token_length': float(np.percentile(token_lengths, 99)),
+        'min_token_length': float(np.min(token_lengths)),
+        'max_token_length': float(np.max(token_lengths)),
+    }
+    
+    print(f"Compression Statistics:")
+    print(f"  Average compression ratio: {stats['compression_ratio']:.2f}x")
+    print(f"  Mean token length: {stats['mean_token_length']:.1f}")
+    print(f"  P99 token length: {stats['p99_token_length']:.0f}")
+    print(f"  Min token length: {stats['min_token_length']:.0f}")
+    print(f"  Max token length: {stats['max_token_length']:.0f}")
+    
+    return stats
+
+
+def main(
+    repo_id: str,
+    root: str | None = None,
+    action_horizon: int = 10,
+    max_episodes: int | None = None,
+    sample_fraction: float = 0.1,
+    encoded_dims: str = "0:6,7:23",
+    delta_dims: str | None = None,
+    use_delta_transform: bool = False,
+    state_key: str = "observation.state",
+    vocab_size: int = 1024,
+    scale: float = 10.0,
+    output_dir: str | None = None,
+):
+    """
+    Train FAST tokenizer for action encoding.
+    
+    Args:
+        repo_id: LeRobot dataset repository ID
+        root: Root directory for dataset (default: ~/.cache/huggingface/lerobot)
+        action_horizon: Number of future actions in each chunk
+        max_episodes: Max episodes to use (None = all episodes in dataset)
+        sample_fraction: Fraction of chunks to sample per episode
+        encoded_dims: Comma-separated dimension ranges to encode (e.g., "0:6,7:23")
+        delta_dims: Comma-separated dimension indices for delta transform (e.g., "0,1,2,3,4,5")
+        use_delta_transform: Whether to apply delta transform (relative actions vs absolute actions)
+        state_key: Dataset key for state observations (default: "observation.state")
+        vocab_size: FAST vocabulary size (BPE vocab size)
+        scale: DCT scaling factor (default: 10.0)
+        output_dir: Directory to save tokenizer (default: ./fast_tokenizer_{repo_id})
+    """
+    # Load dataset
+    print(f"Loading dataset: {repo_id}")
+    dataset = LeRobotDataset(repo_id=repo_id, root=root)
+    print(f"Dataset loaded: {dataset.num_episodes} episodes, {dataset.num_frames} frames")
+    
+    # Parse encoded dimensions
+    encoded_dim_ranges = []
+    for range_str in encoded_dims.split(','):
+        start, end = map(int, range_str.strip().split(':'))
+        encoded_dim_ranges.append((start, end))
+    
+    total_encoded_dims = sum(end - start for start, end in encoded_dim_ranges)
+    print(f"Encoding {total_encoded_dims} dimensions: {encoded_dims}")
+    
+    # Parse delta dimensions
+    delta_dim_list = None
+    if delta_dims is not None and delta_dims.strip():
+        delta_dim_list = [int(d.strip()) for d in delta_dims.split(',')]
+        print(f"Delta dimensions: {delta_dim_list}")
+    else:
+        print("No delta dimensions specified")
+    
+    print(f"Use delta transform: {use_delta_transform}")
+    if use_delta_transform and (delta_dim_list is None or len(delta_dim_list) == 0):
+        print("Warning: use_delta_transform=True but no delta_dims specified. No delta will be applied.")
+    
+    print(f"Action horizon: {action_horizon}")
+    print(f"State key: {state_key}")
+    
+    # Determine episodes to process
+    num_episodes = dataset.num_episodes
+    if max_episodes is not None:
+        num_episodes = min(max_episodes, num_episodes)
+    
+    print(f"Processing {num_episodes} episodes...")
+    
+    # Process episodes sequentially (to avoid pickling issues with dataset)
+    all_chunks = []
+    for ep_idx in range(num_episodes):
+        if ep_idx % 10 == 0:
+            print(f"  Processing episode {ep_idx}/{num_episodes}...")
+        
+        chunks = process_episode(
+            (dataset, ep_idx, action_horizon, delta_dim_list, sample_fraction, state_key, use_delta_transform)
+        )
+        if chunks is not None:
+            all_chunks.append(chunks)
+    
+    # Concatenate all chunks
+    all_chunks = np.concatenate(all_chunks, axis=0)
+    print(f"Collected {len(all_chunks)} action chunks")
+    
+    # Extract only encoded dimensions FIRST (before normalization)
+    encoded_chunks = []
+    for start, end in encoded_dim_ranges:
+        encoded_chunks.append(all_chunks[:, :, start:end])
+    encoded_chunks = np.concatenate(encoded_chunks, axis=-1)  # [N, H, D_encoded]
+    print(f"Extracted {encoded_chunks.shape[-1]} encoded dimensions")
+    
+    # Apply normalization to encoded dimensions only
+    # NOTE: For FAST, we ALWAYS use QUANTILE normalization (no per-timestamp)
+    # This clips outliers and provides consistent [-1, 1] range for DCT compression
+    print(f"\nBefore normalization - overall stats:")
+    print(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
+    print(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
+    
+    norm_stats = dataset.meta.stats
+    if norm_stats is not None and "action" in norm_stats:
+        action_stats = norm_stats["action"]
+        
+        # Build encoded dimension indices
+        encoded_dim_indices = []
+        for start, end in encoded_dim_ranges:
+            encoded_dim_indices.extend(range(start, end))
+        encoded_dim_indices = np.array(encoded_dim_indices)
+        
+        # Use QUANTILE normalization: clip to [q01, q99] and map to [-1, 1]
+        if "q01" in action_stats and "q99" in action_stats:
+            q01 = np.array(action_stats["q01"])[encoded_dim_indices]  # [D_encoded]
+            q99 = np.array(action_stats["q99"])[encoded_dim_indices]  # [D_encoded]
+            
+            print(f"\nNormalization stats (q01, q99) for encoded dimensions:")
+            for i, dim_idx in enumerate(encoded_dim_indices):
+                print(f"  Orig dim {dim_idx}: q01={q01[i]:7.4f}, q99={q99[i]:7.4f}, range={q99[i]-q01[i]:7.4f}")
+            
+            # Clip to quantile range and normalize to [-1, 1]
+            encoded_chunks = np.clip(encoded_chunks, q01, q99)
+            encoded_chunks = 2.0 * (encoded_chunks - q01) / np.maximum(q99 - q01, 1e-6) - 1.0
+            print(f"\nApplied quantile normalization [q01, q99] → [-1, 1]")
+            
+            print(f"\nAfter normalization - overall stats:")
+            print(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
+            print(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
+            
+            print(f"\nPer-dimension stats (after normalization):")
+            for d in range(encoded_chunks.shape[-1]):
+                dim_data = encoded_chunks[:, :, d]
+                print(f"  Dim {d}: min={np.min(dim_data):7.4f}, max={np.max(dim_data):7.4f}, "
+                      f"mean={np.mean(dim_data):7.4f}, std={np.std(dim_data):7.4f}")
+        else:
+            print("Warning: q01/q99 stats not found, using raw actions")
+    else:
+        print("Warning: No normalization stats found, using raw actions")
+    
+    print(f"Encoded chunks shape: {encoded_chunks.shape}")
+    
+    # Train FAST tokenizer
+    tokenizer = train_fast_tokenizer(
+        encoded_chunks,
+        vocab_size=vocab_size,
+        scale=scale,
+    )
+    
+    # Compute compression statistics
+    compression_stats = compute_compression_stats(tokenizer, encoded_chunks)
+    
+    # Save tokenizer
+    if output_dir is None:
+        output_dir = f"fast_tokenizer_{repo_id.replace('/', '_')}"
+    output_path = Path(output_dir)
+    output_path.mkdir(parents=True, exist_ok=True)
+    
+    tokenizer.save_pretrained(output_path)
+    
+    # Save metadata
+    metadata = {
+        'repo_id': repo_id,
+        'vocab_size': vocab_size,
+        'scale': scale,
+        'encoded_dims': encoded_dims,
+        'encoded_dim_ranges': encoded_dim_ranges,
+        'total_encoded_dims': total_encoded_dims,
+        'delta_dims': delta_dims,
+        'delta_dim_list': delta_dim_list,
+        'use_delta_transform': use_delta_transform,
+        'state_key': state_key,
+        'action_horizon': action_horizon,
+        'num_training_chunks': len(encoded_chunks),
+        'compression_stats': compression_stats,
+    }
+    
+    with open(output_path / "metadata.json", 'w') as f:
+        json.dump(metadata, f, indent=2)
+    
+    print(f"\n✅ Saved FAST tokenizer to {output_path}")
+    print(f"Metadata: {json.dumps(metadata, indent=2)}")
+
+
+if __name__ == "__main__":
+    tyro.cli(main)

src/lerobot/policies/pi05/train_fast_tokenizer_example.md

@@ -0,0 +1,101 @@
+# Train FAST Tokenizer - Usage Examples
+
+This script trains a FAST (Factorized Action Sequence Tokenizer) on LeRobotDataset action data.
+
+## Basic Usage
+
+```bash
+python src/lerobot/policies/pi05/train_fast_tokenizer.py \
+    --repo_id "lerobot/aloha_sim_insertion_human" \
+    --action_horizon 10 \
+    --encoded_dims "0:7" \
+    --vocab_size 1024 \
+    --scale 10.0
+```
+
+## Parameters
+
+### Required
+- `--repo_id`: LeRobot dataset repository ID (e.g., "lerobot/aloha_sim_insertion_human")
+
+### Optional
+- `--root`: Root directory for dataset (default: ~/.cache/huggingface/lerobot)
+- `--action_horizon`: Number of future actions in each chunk (default: 10)
+- `--max_episodes`: Maximum number of episodes to use (default: None = all)
+- `--sample_fraction`: Fraction of chunks to sample per episode (default: 0.1)
+- `--encoded_dims`: Comma-separated dimension ranges to encode (default: "0:6,7:23")
+  - Example: "0:7" encodes dimensions 0-6
+  - Example: "0:3,6:9" encodes dimensions 0-2 and 6-8
+- `--delta_dims`: Comma-separated dimension indices for delta transform (default: None)
+  - Example: "0,1,2,3,4,5" applies delta transform to first 6 dimensions
+  - Delta transform: action[i] - state[i] for specified dimensions
+- `--state_key`: Dataset key for state observations (default: "observation.state")
+- `--vocab_size`: FAST vocabulary size / BPE vocab size (default: 1024)
+- `--scale`: DCT scaling factor (default: 10.0)
+- `--output_dir`: Directory to save tokenizer (default: ./fast_tokenizer_{repo_id})
+
+## Examples
+
+### Example 1: Train on full action space
+
+```bash
+python src/lerobot/policies/pi05/train_fast_tokenizer.py \
+    --repo_id "lerobot/pusht" \
+    --action_horizon 16 \
+    --encoded_dims "0:2" \
+    --vocab_size 512 \
+    --max_episodes 100
+```
+
+### Example 2: Train with delta transform
+
+```bash
+python src/lerobot/policies/pi05/train_fast_tokenizer.py \
+    --repo_id "lerobot/aloha_sim_insertion_human" \
+    --action_horizon 10 \
+    --encoded_dims "0:14" \
+    --delta_dims "0,1,2,3,4,5,6,7,8,9,10,11,12,13" \
+    --state_key "observation.state" \
+    --vocab_size 1024 \
+    --scale 10.0 \
+    --sample_fraction 0.2
+```
+
+### Example 3: Train on subset of dimensions
+
+```bash
+python src/lerobot/policies/pi05/train_fast_tokenizer.py \
+    --repo_id "lerobot/aloha_sim_insertion_human" \
+    --action_horizon 10 \
+    --encoded_dims "0:7" \
+    --vocab_size 1024 \
+    --output_dir "./my_tokenizer"
+```
+
+## Output
+
+The script saves:
+1. **Tokenizer files**: Trained FAST tokenizer (can be loaded with `AutoProcessor.from_pretrained()`)
+2. **metadata.json**: Contains:
+   - Configuration parameters
+   - Compression statistics (compression ratio, token lengths)
+   - Training dataset information
+
+## Understanding the Process
+
+1. **Load Dataset**: Loads the LeRobotDataset from HuggingFace
+2. **Extract Action Chunks**: Creates sliding windows of actions with specified horizon
+3. **Apply Delta Transform**: (Optional) Computes action deltas relative to current state
+4. **Select Encoded Dimensions**: Extracts only the dimensions to be encoded
+5. **Normalize**: Applies quantile normalization ([q01, q99] → [-1, 1])
+6. **Train Tokenizer**: Trains BPE tokenizer on DCT coefficients
+7. **Compute Stats**: Reports compression ratio and token length statistics
+8. **Save**: Saves tokenizer and metadata
+
+## Notes
+
+- **Normalization**: The script uses quantile normalization (q01, q99) from the dataset's statistics
+- **Sampling**: To speed up training, you can sample a fraction of chunks per episode
+- **Delta Transform**: Applied per-dimension to make actions relative to current state
+- **Compression**: FAST uses DCT + BPE to compress action sequences efficiently
+

src/lerobot/policies/pi05/train_multi.sh

@@ -0,0 +1,23 @@
+rm -rf /fsx/jade_choghari/outputs/pi0_multi_training
+accelerate launch --multi_gpu --num_processes=2 \
+    $(which lerobot-train) \
+    --dataset.repo_id=local \
+    --dataset.root=/fsx/jade_choghari/outputs/collect-data-pgen \
+    --output_dir=/fsx/jade_choghari/outputs/pi0_multi_training \
+    --job_name=pi0_multi_training \
+    --policy.repo_id=jadechoghari/pi0-base1 \
+    --policy.path=lerobot/pi05_base \
+    --policy.dtype=bfloat16 \
+    --steps=50000 \
+    --save_freq=5000 \
+    --rename_map='{
+        "observation.images.base": "observation.images.base_0_rgb",
+        "observation.images.left_wrist": "observation.images.left_wrist_0_rgb",
+        "observation.images.right_wrist": "observation.images.right_wrist_0_rgb",
+        }' \
+    --policy.gradient_checkpointing=true \
+    --batch_size=1 \
+    --policy.device=cpu
+    # --wandb.enable=true \
+    # --wandb.disable_artifact=true \
+    # --wandb.project=pi05hi-training \

src/lerobot/processor/__init__.py

@@ -75,7 +75,7 @@ from .policy_robot_bridge import (
     RobotActionToPolicyActionProcessorStep,
 )
 from .rename_processor import RenameObservationsProcessorStep
-from .tokenizer_processor import TokenizerProcessorStep
+from .tokenizer_processor import TokenizerProcessorStep, ActionTokenizerProcessorStep
 
 __all__ = [
     "ActionProcessorStep",

src/lerobot/processor/tokenizer_processor.py

@@ -15,10 +15,13 @@
 # limitations under the License.
 
 """
-This script defines a processor for tokenizing natural language instructions from an environment transition.
+This script defines processors for tokenizing data from an environment transition.
 
-It uses a tokenizer from the Hugging Face `transformers` library to convert task descriptions (text) into
-token IDs and attention masks, which are then added to the observation dictionary.
+It includes:
+- TokenizerProcessorStep: Uses a tokenizer from the Hugging Face `transformers` library to convert 
+  task descriptions (text) into token IDs and attention masks, which are then added to the observation dictionary.
+- ActionTokenizerProcessorStep: Uses a processor/tokenizer (e.g., the Physical Intelligence "fast" tokenizer)
+  to tokenize action tensors into discrete token IDs for action modeling.
 """
 
 from __future__ import annotations
@@ -30,6 +33,8 @@ import torch
 
 from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
 from lerobot.utils.constants import (
+    ACTION_TOKEN_MASK,
+    ACTION_TOKENS,
     OBS_LANGUAGE_ATTENTION_MASK,
     OBS_LANGUAGE_HIGH_LEVEL_TASK_ATTENTION_MASK,
     OBS_LANGUAGE_HIGH_LEVEL_TASK_TOKENS,
@@ -40,12 +45,13 @@ from lerobot.utils.constants import (
 from lerobot.utils.import_utils import _transformers_available
 
 from .core import EnvTransition, TransitionKey
-from .pipeline import ObservationProcessorStep, ProcessorStepRegistry
+from .pipeline import ActionProcessorStep, ObservationProcessorStep, ProcessorStepRegistry
 
 # Conditional import for type checking and lazy loading
 if TYPE_CHECKING or _transformers_available:
-    from transformers import AutoTokenizer
+    from transformers import AutoProcessor, AutoTokenizer
 else:
+    AutoProcessor = None
     AutoTokenizer = None
 
 
@@ -302,15 +308,17 @@ class TokenizerProcessorStep(ObservationProcessorStep):
 
     def _tokenize_text(self, text: str | list[str]) -> dict[str, torch.Tensor]:
         """
-        A wrapper around the tokenizer call.
+        A wrapper around the tokenizer call that appends an EOS token to each sequence.
 
         Args:
             text: A string or list of strings to tokenize.
 
         Returns:
-            A dictionary containing tokenized 'input_ids' and 'attention_mask' as PyTorch tensors.
+            A dictionary containing tokenized 'input_ids' and 'attention_mask' as PyTorch tensors,
+            with EOS token appended at the end of each sequence.
         """
-        return self.input_tokenizer(
+        # Tokenize normally
+        tokenized = self.input_tokenizer(
             text,
             max_length=self.max_length,
             truncation=self.truncation,
@@ -318,6 +326,34 @@ class TokenizerProcessorStep(ObservationProcessorStep):
             padding_side=self.padding_side,
             return_tensors="pt",
         )
+        
+        # Get EOS token ID
+        eos_token_id = self.input_tokenizer.eos_token_id
+        if eos_token_id is None:
+            # Some tokenizers don't have an EOS token, skip modification
+            return tokenized
+        
+        # Append EOS token to each sequence (before padding)
+        input_ids = tokenized["input_ids"]
+        attention_mask = tokenized["attention_mask"]
+        
+        for i in range(input_ids.shape[0]):
+            # Find the position of the last non-padding token
+            non_pad_positions = (attention_mask[i] == 1).nonzero(as_tuple=True)[0]
+            
+            if len(non_pad_positions) > 0:
+                last_token_pos = non_pad_positions[-1].item()
+                
+                # Check if there's room to add EOS token
+                if last_token_pos + 1 < self.max_length:
+                    # Insert EOS token after the last real token
+                    input_ids[i, last_token_pos + 1] = eos_token_id
+                    attention_mask[i, last_token_pos + 1] = 1
+                else:
+                    # If at max length, replace the last token with EOS
+                    input_ids[i, last_token_pos] = eos_token_id
+        
+        return {"input_ids": input_ids, "attention_mask": attention_mask}
 
     def get_config(self) -> dict[str, Any]:
         """
@@ -393,3 +429,233 @@ class TokenizerProcessorStep(ObservationProcessorStep):
             )
 
         return features
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="action_tokenizer_processor")
+class ActionTokenizerProcessorStep(ActionProcessorStep):
+    """
+    Processor step to tokenize action data using a fast action tokenizer.
+
+    This step takes action tensors from an `EnvTransition`, tokenizes them using
+    a Hugging Face `transformers` AutoProcessor (such as the Physical Intelligence "fast" tokenizer),
+    and returns the tokenized action.
+
+    Requires the `transformers` library to be installed.
+
+    Attributes:
+        tokenizer_name: The name of a pretrained processor from the Hugging Face Hub (e.g., "physical-intelligence/fast").
+        tokenizer: A pre-initialized processor/tokenizer object. If provided, `tokenizer_name` is ignored.
+        trust_remote_code: Whether to trust remote code when loading the tokenizer (required for some tokenizers).
+        action_tokenizer: The internal tokenizer/processor instance, loaded during initialization.
+    """
+
+    tokenizer_name: str | None = None
+    tokenizer: Any | None = None
+    trust_remote_code: bool = True
+    max_action_tokens: int = 32
+    # Internal tokenizer instance (not part of the config)
+    action_tokenizer: Any = field(default=None, init=False, repr=False)
+
+    def __post_init__(self):
+        """
+        Initializes the action tokenizer after the dataclass is created.
+
+        It checks for the availability of the `transformers` library and loads the tokenizer
+        either from a provided object or by name from the Hugging Face Hub.
+
+        Raises:
+            ImportError: If the `transformers` library is not installed.
+            ValueError: If neither `tokenizer` nor `tokenizer_name` is provided.
+        """
+        if not _transformers_available:
+            raise ImportError(
+                "The 'transformers' library is not installed. "
+                "Please install it with `pip install 'lerobot[transformers-dep]'` to use ActionTokenizerProcessorStep."
+            )
+
+        if self.tokenizer is not None:
+            # Use provided tokenizer object directly
+            self.action_tokenizer = self.tokenizer
+        elif self.tokenizer_name is not None:
+            if AutoProcessor is None:
+                raise ImportError("AutoProcessor is not available")
+            self.action_tokenizer = AutoProcessor.from_pretrained(
+                self.tokenizer_name, trust_remote_code=self.trust_remote_code
+            )
+        else:
+            raise ValueError(
+                "Either 'tokenizer' or 'tokenizer_name' must be provided. "
+                "Pass a tokenizer object directly or a tokenizer name to auto-load."
+            )
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        """
+        Applies action tokenization to the transition.
+        
+        This overrides the base class to handle both tokens and mask.
+        
+        Args:
+            transition: The input transition with action data.
+            
+        Returns:
+            The processed transition with tokenized actions and mask in complementary data.
+        """
+        self._current_transition = transition.copy()
+        new_transition = self._current_transition
+
+        action = new_transition.get(TransitionKey.ACTION)
+        if action is None:
+            raise ValueError("ActionTokenizerProcessorStep requires an action in the transition.")
+
+        # Tokenize and get both tokens and mask
+        tokens, mask = self._tokenize_action(action)
+        
+        # Store mask in complementary data
+        complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
+        if complementary_data is None:
+            complementary_data = {}
+        complementary_data[ACTION_TOKEN_MASK] = mask
+        complementary_data[ACTION_TOKENS] = tokens
+        new_transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data
+        return new_transition
+
+    def _tokenize_action(self, action: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Tokenizes the action tensor and creates a mask.
+
+        Args:
+            action: The input action tensor to tokenize. Shape: (B, action_dim) or (action_dim,)
+
+        Returns:
+            A tuple of (tokens, mask) where:
+            - tokens: Tensor of token IDs with shape (B, max_action_tokens)
+            - mask: Boolean mask with shape (B, max_action_tokens), True for real tokens, False for padding
+        """
+        if action is None:
+            raise ValueError("Action cannot be None")
+
+        # Get the device and dtype of the input action
+        device = action.device if isinstance(action, torch.Tensor) else None
+        
+        # Handle single sample (add batch dimension)
+        single_sample = action.dim() == 1
+        if single_sample:
+            action = action.unsqueeze(0)
+        
+        batch_size = action.shape[0]
+        
+        # Tokenize the action batch
+        # The fast tokenizer expects action data and returns token IDs
+        tokens_list = []
+        masks_list = []
+        
+        for i in range(batch_size):
+            # Tokenize single action (move to CPU first as tokenizer uses scipy which requires numpy)
+            action_cpu = action[i:i+1].cpu()
+            tokens = self.action_tokenizer(action_cpu)
+            
+            # Convert to numpy array if it's a list
+            if isinstance(tokens, list):
+                tokens = torch.tensor(tokens, dtype=torch.long, device=action.device)
+            elif not isinstance(tokens, torch.Tensor):
+                tokens = torch.tensor(tokens, dtype=torch.long, device=action.device)
+            else:
+                # Move tokens back to the same device as input action
+                tokens = tokens.to(device=action.device)
+            
+            # Flatten to 1D if needed
+            if tokens.dim() > 1:
+                tokens = tokens.flatten()
+            
+            # Truncate or pad to max_action_tokens
+            if len(tokens) > self.max_action_tokens:
+                tokens = tokens[:self.max_action_tokens]
+                mask = torch.ones(self.max_action_tokens, dtype=torch.bool, device=action.device)
+            else:
+                mask = torch.cat([
+                    torch.ones(len(tokens), dtype=torch.bool, device=action.device),
+                    torch.zeros(self.max_action_tokens - len(tokens), dtype=torch.bool, device=action.device)
+                ])
+                # Pad tokens with zeros
+                tokens = torch.nn.functional.pad(
+                    tokens, 
+                    (0, self.max_action_tokens - len(tokens)), 
+                    value=0
+                )
+            
+            tokens_list.append(tokens)
+            masks_list.append(mask)
+        
+        # Stack into batched tensors
+        tokens_batch = torch.stack(tokens_list, dim=0)  # (B, max_action_tokens)
+        masks_batch = torch.stack(masks_list, dim=0)    # (B, max_action_tokens)
+        
+        # Remove batch dimension if input was single sample
+        if single_sample:
+            tokens_batch = tokens_batch.squeeze(0)
+            masks_batch = masks_batch.squeeze(0)
+        
+        # Move to the same device as the input
+        if device is not None:
+            tokens_batch = tokens_batch.to(device)
+            masks_batch = masks_batch.to(device)
+
+        return tokens_batch, masks_batch
+
+    def action(self, action: torch.Tensor) -> torch.Tensor:
+        """
+        This method is not used since we override __call__.
+        Required by ActionProcessorStep ABC.
+        """
+        tokens, _ = self._tokenize_action(action)
+        return tokens
+
+    def get_config(self) -> dict[str, Any]:
+        """
+        Returns the serializable configuration of the processor.
+
+        Note: The tokenizer object itself is not serialized. If the processor was initialized
+        with a tokenizer name, that name will be included in the config.
+
+        Returns:
+            A dictionary with the processor's configuration parameters.
+        """
+        config = {
+            "trust_remote_code": self.trust_remote_code,
+            "max_action_tokens": self.max_action_tokens,
+        }
+
+        # Only save tokenizer_name if it was used to create the tokenizer
+        if self.tokenizer_name is not None and self.tokenizer is None:
+            config["tokenizer_name"] = self.tokenizer_name
+
+        return config
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        """
+        Updates feature definitions to reflect tokenized actions.
+
+        This updates the policy features dictionary to indicate that the action
+        has been tokenized into a sequence of token IDs with shape (max_action_tokens,).
+
+        Args:
+            features: The dictionary of existing policy features.
+
+        Returns:
+            The updated dictionary of policy features.
+        """
+        # Update the action feature to reflect the tokenized shape
+        # The action is now a sequence of token IDs
+        if PipelineFeatureType.ACTION in features:
+            # Replace the action feature with the tokenized version
+            features[PipelineFeatureType.ACTION] = {
+                ACTION_TOKENS: PolicyFeature(
+                    type=FeatureType.SEQUENCE,  # Token sequence
+                    shape=(self.max_action_tokens,)
+                )
+            }
+        
+        return features

src/lerobot/utils/constants.py

@@ -33,6 +33,8 @@ OBS_LANGUAGE_SUBTASK_ONLY = OBS_STR + ".subtask"
 OBS_LANGUAGE_SUBTASK_ONLY_TOKENS = OBS_LANGUAGE_SUBTASK_ONLY + ".tokens"
 OBS_LANGUAGE_SUBTASK_ONLY_ATTENTION_MASK = OBS_LANGUAGE_SUBTASK_ONLY + ".attention_mask"
 ACTION = "action"
+ACTION_TOKENS = ACTION + ".tokens"
+ACTION_TOKEN_MASK = ACTION + ".token_mask"
 REWARD = "next.reward"
 TRUNCATED = "next.truncated"
 DONE = "next.done"