feat(dagger): adding smooth handover (#3506)

* feat(dagger): adding smooth handover


* update docstring


* small phase fix and documenting potential issues


* cleaning up

.github/workflows/stale.yml

@@ -24,14 +24,14 @@ on:
 
 env:
   CLOSE_ISSUE_MESSAGE: >
-    This issue was closed because it has been stalled for 14 days with no activity.
+    This issue was closed because it has been stalled for 30 days with no activity.
     Feel free to reopen if is still relevant, or to ping a collaborator if you have any questions.
   CLOSE_PR_MESSAGE: >
-    This PR was closed because it has been stalled for 21 days with no activity.
+    This PR was closed because it has been stalled for 30 days with no activity.
     Feel free to reopen if is still relevant, or to ping a collaborator if you have any questions.
   WARN_ISSUE_MESSAGE: >
     This issue has been automatically marked as stale because it has not had
-    recent activity (6 months). It will be closed if no further activity occurs.
+    recent activity (1 year). It will be closed if no further activity occurs.
     Any change, comment or update to this issue will reset this count.
     Thank you for your contributions.
   WARN_PR_MESSAGE: >
@@ -59,10 +59,10 @@ jobs:
           stale-pr-label: stale
           exempt-issue-labels: never-stale
           exempt-pr-labels: never-stale
-          days-before-issue-stale: 180
-          days-before-issue-close: 14
+          days-before-issue-stale: 365
+          days-before-issue-close: 30
           days-before-pr-stale: 365
-          days-before-pr-close: 21
+          days-before-pr-close: 30
           delete-branch: true
           close-issue-message: ${{ env.CLOSE_ISSUE_MESSAGE }}
           close-pr-message: ${{ env.CLOSE_PR_MESSAGE }}

docker/Dockerfile.internal

@@ -18,9 +18,8 @@
 # docker build -f docker/Dockerfile.internal -t lerobot-internal .
 
 # Configure the base image for CI with GPU access
-# TODO(Steven): Bump these versions
-ARG CUDA_VERSION=12.4.1
-ARG OS_VERSION=22.04
+ARG CUDA_VERSION=12.6.3
+ARG OS_VERSION=24.04
 FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu${OS_VERSION}
 
 # Define Python version argument
@@ -36,16 +35,13 @@ ENV DEBIAN_FRONTEND=noninteractive \
 
 # Install Python, system dependencies, and uv (as root)
 RUN apt-get update && apt-get install -y --no-install-recommends \
-    software-properties-common build-essential git curl \
-    libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
+    build-essential git curl \
+    libglib2.0-0 libgl1 libegl1 ffmpeg \
     libusb-1.0-0-dev speech-dispatcher libgeos-dev portaudio19-dev \
     cmake pkg-config ninja-build \
-    && add-apt-repository -y ppa:deadsnakes/ppa \
-    && apt-get update \
-    && apt-get install -y --no-install-recommends \
-       python${PYTHON_VERSION} \
-       python${PYTHON_VERSION}-venv \
-       python${PYTHON_VERSION}-dev \
+    python${PYTHON_VERSION} \
+    python${PYTHON_VERSION}-venv \
+    python${PYTHON_VERSION}-dev \
     && curl -LsSf https://astral.sh/uv/install.sh | sh \
     && mv /root/.local/bin/uv /usr/local/bin/uv \
     && useradd --create-home --shell /bin/bash user_lerobot \

pyproject.toml

@@ -128,7 +128,7 @@ dataset_viz = ["lerobot[dataset]", "lerobot[viz]"]
 av-dep = ["av>=15.0.0,<16.0.0"]
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
 placo-dep = ["placo>=0.9.6,<0.9.17"]
-transformers-dep = ["transformers==5.3.0"] # TODO(Steven): https://github.com/huggingface/lerobot/pull/3249
+transformers-dep = ["transformers>=5.4.0,<5.6.0"]
 grpcio-dep = ["grpcio==1.73.1", "protobuf>=6.31.1,<6.32.0"]
 can-dep = ["python-can>=4.2.0,<5.0.0"]
 peft-dep = ["peft>=0.18.0,<1.0.0"]

src/lerobot/policies/factory.py

@@ -514,7 +514,7 @@ def make_policy(
 
         logging.info("Loading policy's PEFT adapter.")
 
-        peft_pretrained_path = cfg.pretrained_path
+        peft_pretrained_path = str(cfg.pretrained_path)
         peft_config = PeftConfig.from_pretrained(peft_pretrained_path)
 
         kwargs["pretrained_name_or_path"] = peft_config.base_model_name_or_path
@@ -527,7 +527,9 @@ def make_policy(
             )
 
         policy = policy_cls.from_pretrained(**kwargs)
-        policy = PeftModel.from_pretrained(policy, peft_pretrained_path, config=peft_config)
+        policy = PeftModel.from_pretrained(
+            policy, peft_pretrained_path, config=peft_config, is_trainable=True
+        )
 
     else:
         # Make a fresh policy.

src/lerobot/policies/groot/action_head/flow_matching_action_head.py

@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import dataclass, field
+from dataclasses import field
 from typing import TYPE_CHECKING
 
 import torch
@@ -109,7 +109,6 @@ class MultiEmbodimentActionEncoder(nn.Module):
         return x
 
 
-@dataclass
 class FlowmatchingActionHeadConfig(PretrainedConfig):
     """NOTE: N1.5 uses XEmbFlowmatchingPolicyHeadConfig as action head"""
 

src/lerobot/policies/pi0/modeling_pi0.py

@@ -444,13 +444,13 @@ class PaliGemmaWithExpertModel(
         if image.dtype != torch.float32:
             image = image.to(torch.float32)
         image_outputs = self.paligemma.model.get_image_features(image)
-        features = image_outputs.pooler_output * self.paligemma.config.text_config.hidden_size**0.5
+        features = image_outputs.pooler_output
         if features.dtype != out_dtype:
             features = features.to(out_dtype)
         return features
 
     def embed_language_tokens(self, tokens: torch.Tensor):
-        return self.paligemma.model.language_model.embed_tokens(tokens)
+        return self.paligemma.model.language_model.get_input_embeddings()(tokens)
 
     def forward(
         self,
@@ -666,8 +666,7 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         # Process language tokens
         def lang_embed_func(lang_tokens):
             lang_emb = self.paligemma_with_expert.embed_language_tokens(lang_tokens)
-            lang_emb_dim = lang_emb.shape[-1]
-            return lang_emb * math.sqrt(lang_emb_dim)
+            return lang_emb
 
         lang_emb = self._apply_checkpoint(lang_embed_func, lang_tokens)
         embs.append(lang_emb)
@@ -748,16 +747,8 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
 
         return embs, pad_masks, att_masks, adarms_cond
 
-    def forward(
-        self, images, img_masks, lang_tokens, lang_masks, state, actions, noise=None, time=None
-    ) -> Tensor:
+    def forward(self, images, img_masks, lang_tokens, lang_masks, state, actions, noise, time) -> Tensor:
         """Do a full training forward pass and compute the loss."""
-        if noise is None:
-            noise = self.sample_noise(actions.shape, actions.device)
-
-        if time is None:
-            time = self.sample_time(actions.shape[0], actions.device)
-
         time_expanded = time[:, None, None]
         x_t = time_expanded * noise + (1 - time_expanded) * actions
         u_t = noise - actions
@@ -1292,8 +1283,11 @@ class PI0Policy(PreTrainedPolicy):
         state = self.prepare_state(batch)
         actions = self.prepare_action(batch)
 
+        noise = self.model.sample_noise(actions.shape, actions.device)
+        time = self.model.sample_time(actions.shape[0], actions.device)
+
         # Compute loss
-        losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions)
+        losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions, noise, time)
 
         # Truncate losses to actual action dimensions
         original_action_dim = self.config.output_features[ACTION].shape[0]

src/lerobot/policies/pi05/modeling_pi05.py

@@ -728,14 +728,8 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
 
         return embs, pad_masks, att_masks, adarms_cond
 
-    def forward(self, images, img_masks, tokens, masks, actions, noise=None, time=None) -> Tensor:
+    def forward(self, images, img_masks, tokens, masks, actions, noise, time) -> Tensor:
         """Do a full training forward pass and compute the loss."""
-        if noise is None:
-            noise = self.sample_noise(actions.shape, actions.device)
-
-        if time is None:
-            time = self.sample_time(actions.shape[0], actions.device)
-
         time_expanded = time[:, None, None]
         x_t = time_expanded * noise + (1 - time_expanded) * actions
         u_t = noise - actions
@@ -1262,8 +1256,11 @@ class PI05Policy(PreTrainedPolicy):
 
         actions = self.prepare_action(batch)
 
+        noise = self.model.sample_noise(actions.shape, actions.device)
+        time = self.model.sample_time(actions.shape[0], actions.device)
+
         # Compute loss (no separate state needed for PI05)
-        losses = self.model.forward(images, img_masks, tokens, masks, actions)
+        losses = self.model.forward(images, img_masks, tokens, masks, actions, noise, time)
 
         # Truncate losses to actual action dimensions
         original_action_dim = self.config.output_features[ACTION].shape[0]

src/lerobot/policies/pi0_fast/modeling_pi0_fast.py

@@ -16,7 +16,6 @@
 
 import builtins
 import logging
-import math
 from collections import deque
 from pathlib import Path
 from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
@@ -261,13 +260,15 @@ class PI0FastPaliGemma(nn.Module):
         if image.dtype != torch.float32:
             image = image.to(torch.float32)
         image_outputs = self.paligemma.model.get_image_features(image)
-        features = image_outputs.pooler_output * self.paligemma.config.text_config.hidden_size**0.5
+        features = image_outputs.pooler_output
+        norm = 2048**0.5
+        features = features / norm * norm
         if features.dtype != out_dtype:
             features = features.to(out_dtype)
         return features
 
     def embed_language_tokens(self, tokens: torch.Tensor):
-        return self.paligemma.model.language_model.embed_tokens(tokens)
+        return self.paligemma.model.language_model.get_input_embeddings()(tokens)
 
     def forward(
         self,
@@ -417,8 +418,7 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
         # Process language instruction tokens
         def lang_embed_func(tokens):
             lang_emb = self.paligemma_with_expert.embed_language_tokens(tokens)
-            lang_emb_dim = lang_emb.shape[-1]
-            return lang_emb * math.sqrt(lang_emb_dim)
+            return lang_emb
 
         lang_emb = self._apply_checkpoint(lang_embed_func, tokens)
         embs.append(lang_emb)
@@ -432,8 +432,7 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
 
             def fast_action_embed_func(fast_action_tokens):
                 fast_emb = self.paligemma_with_expert.embed_language_tokens(fast_action_tokens)
-                fast_emb_dim = fast_emb.shape[-1]
-                return fast_emb * math.sqrt(fast_emb_dim)
+                return fast_emb
 
             fast_action_emb = self._apply_checkpoint(fast_action_embed_func, fast_action_tokens)
             embs.append(fast_action_emb)
@@ -666,7 +665,6 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
             if t < max_decoding_steps - 1:
                 # embed the newly generated token
                 next_token_emb = self.paligemma_with_expert.embed_language_tokens(next_token)
-                next_token_emb = next_token_emb * math.sqrt(next_token_emb.shape[-1])
                 if prefix_embs.dtype == torch.bfloat16:
                     next_token_emb = next_token_emb.to(dtype=torch.bfloat16)
 
@@ -771,7 +769,6 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
             # Embed the single previous token
             # We use embed_language_tokens directly to avoid overhead of full prefix embedding
             next_token_emb = self.paligemma_with_expert.embed_language_tokens(next_token)
-            next_token_emb = next_token_emb * math.sqrt(next_token_emb.shape[-1])
             if prefix_embs.dtype == torch.bfloat16:
                 next_token_emb = next_token_emb.to(dtype=torch.bfloat16)
 

src/lerobot/policies/wall_x/qwen_model/qwen2_5_vl_moe.py

@@ -22,7 +22,7 @@ from transformers.utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     is_flash_attn_2_available,
-    is_flash_attn_greater_or_equal_2_10,
+    is_flash_attn_greater_or_equal,
     is_torchdynamo_compiling,
     logging,
     replace_return_docstrings,
@@ -890,7 +890,7 @@ class Qwen2_5_VLFlashAttention2(Qwen2_5_VLAttention):
         # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
         # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
         # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal("2.1.0")
 
     def forward(
         self,

src/lerobot/policies/xvla/modeling_florence2.py

@@ -45,7 +45,7 @@ from transformers.utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     is_flash_attn_2_available,
-    is_flash_attn_greater_or_equal_2_10,
+    is_flash_attn_greater_or_equal,
     logging,
     replace_return_docstrings,
 )
@@ -909,7 +909,7 @@ class Florence2FlashAttention2(Florence2Attention):
         # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
         # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
         # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal("2.1.0")
 
     def _reshape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
         return tensor.view(bsz, seq_len, self.num_heads, self.head_dim)

src/lerobot/rollout/strategies/dagger.py

@@ -33,12 +33,13 @@ Recording modes:
     ``record_autonomous=False``: Only correction windows are recorded.
         Each correction (start to stop) becomes one episode.
 
-Teleoperator expectations:
-    The user is responsible for keeping the leader arm aligned with the
-    follower arm at the moment a correction begins.  Programmatic motor
-    handover (``enable_torque`` / ``disable_torque`` / ``write_goal_positions``)
-    is intentionally not invoked here — see the TODO in
-    :func:`DAggerStrategy._apply_transition` for the open design decision.
+Teleoperator handover:
+    On AUTONOMOUS → PAUSED, actuated teleops (those with non-empty
+    ``feedback_features``, e.g. SO-101, OpenArmMini) are smoothly driven to
+    the follower's last position via ``send_feedback`` so the operator takes
+    over without a jerk.  Non-actuated teleops cannot be driven,
+    so on PAUSED → CORRECTING the follower is instead slid to the teleop's
+    current pose before the correction begins.
 """
 
 from __future__ import annotations
@@ -175,17 +176,27 @@ class DAggerEvents:
 # ---------------------------------------------------------------------------
 
 
-# TODO(Steven): re-enable programmatic teleop alignment once we decide whether
-# to enforce motor-control methods on every Teleoperator.  Until then the user
-# is responsible for moving the leader arm to the follower's pose at the moment
-# a correction begins.
-def _teleop_smooth_move_to(
-    teleop: Teleoperator, target_pos: dict, duration_s: float = 2.0, fps: int = 50
-) -> None:
-    """Smoothly move teleop to target position via linear interpolation.
+def _teleop_supports_feedback(teleop: Teleoperator) -> bool:
+    """Return True when the teleop can receive position feedback (is actuated).
+    TODO(Maxime): See if it is possible to unify this interface across teleops instead of duck-typing.
+    """
+    return (
+        bool(teleop.feedback_features)
+        and hasattr(teleop, "disable_torque")
+        and hasattr(teleop, "enable_torque")
+    )
 
-    Requires the teleoperator to support motor control methods
-    (``enable_torque``, ``write_goal_positions``, ``get_action``).
+
+def _teleop_smooth_move_to(
+    teleop: Teleoperator, target_pos: dict, duration_s: float = 2.0, fps: int = 30
+) -> None:
+    """Smoothly move an actuated teleop to ``target_pos`` via linear interpolation.
+
+    Requires the teleoperator to support feedback
+    (i.e. have non-empty ``feedback_features`` and implement ``disable_torque`` / ``enable_torque``).
+
+    TODO(Maxime): This blocks up to ``duration_s`` seconds, during this time
+    the follower robot doesn't receive new actions, this could be an issue on LeKiwi.
     """
     teleop.enable_torque()
     current = teleop.get_action()
@@ -193,13 +204,28 @@ def _teleop_smooth_move_to(
 
     for step in range(steps + 1):
         t = step / steps
-        interp = {}
-        for k in current:
-            if k in target_pos:
-                interp[k] = current[k] * (1 - t) + target_pos[k] * t
-            else:
-                interp[k] = current[k]
-        teleop.write_goal_positions(interp)
+        interp = {
+            k: current[k] * (1 - t) + target_pos[k] * t if k in target_pos else current[k] for k in current
+        }
+        teleop.send_feedback(interp)
+        time.sleep(1 / fps)
+
+
+def _follower_smooth_move_to(
+    robot: ThreadSafeRobot, current: dict, target: dict, duration_s: float = 1.0, fps: int = 30
+) -> None:
+    """Smoothly move the follower robot from ``current`` to ``target`` action.
+
+    Used when the teleop is non-actuated: instead of driving the leader arm
+    to the follower, we bring the follower to the teleop's current pose.
+    Both ``current`` and ``target`` must be in robot-action key space.
+    """
+    steps = max(int(duration_s * fps), 1)
+
+    for step in range(steps + 1):
+        t = step / steps
+        interp = {k: current[k] * (1 - t) + target[k] * t if k in target else current[k] for k in current}
+        robot.send_action(interp)
         time.sleep(1 / fps)
 
 
@@ -415,9 +441,6 @@ class DAggerStrategy(RolloutStrategy):
         engine.reset()
         interpolator.reset()
         events.reset()
-        # TODO(Steven): re-enable once Teleoperator motor-control methods are
-        # standardised; until then the user pre-aligns the leader by hand.
-        # teleop.disable_torque()
         engine.resume()
 
         last_action: dict[str, Any] | None = None
@@ -441,8 +464,16 @@ class DAggerStrategy(RolloutStrategy):
                     transition = events.consume_transition()
                     if transition is not None:
                         old_phase, new_phase = transition
-                        self._apply_transition(old_phase, new_phase, engine, interpolator, robot, teleop)
-                        last_action = None
+                        self._apply_transition(
+                            old_phase,
+                            new_phase,
+                            engine,
+                            interpolator,
+                            ctx,
+                            last_action,
+                        )
+                        if new_phase == DAggerPhase.AUTONOMOUS:
+                            last_action = None
 
                     phase = events.phase
                     obs = robot.get_observation()
@@ -532,9 +563,6 @@ class DAggerStrategy(RolloutStrategy):
             finally:
                 logger.info("DAgger continuous control loop ended — pausing engine")
                 engine.pause()
-                # TODO(Steven): re-enable once Teleoperator motor-control methods
-                # are standardised across all teleop implementations.
-                # teleop.disable_torque()
                 with contextlib.suppress(Exception):
                     with self._episode_lock:
                         dataset.save_episode()
@@ -570,9 +598,6 @@ class DAggerStrategy(RolloutStrategy):
         engine.reset()
         interpolator.reset()
         events.reset()
-        # TODO(Steven): re-enable once Teleoperator motor-control methods are
-        # standardised; until then the user pre-aligns the leader by hand.
-        # teleop.disable_torque()
         engine.resume()
 
         last_action: dict[str, Any] | None = None
@@ -600,8 +625,16 @@ class DAggerStrategy(RolloutStrategy):
                     transition = events.consume_transition()
                     if transition is not None:
                         old_phase, new_phase = transition
-                        self._apply_transition(old_phase, new_phase, engine, interpolator, robot, teleop)
-                        last_action = None
+                        self._apply_transition(
+                            old_phase,
+                            new_phase,
+                            engine,
+                            interpolator,
+                            ctx,
+                            last_action,
+                        )
+                        if new_phase == DAggerPhase.AUTONOMOUS:
+                            last_action = None
 
                         # Correction ended -> save episode (blocking if not streaming)
                         if old_phase == DAggerPhase.CORRECTING and new_phase == DAggerPhase.PAUSED:
@@ -679,9 +712,6 @@ class DAggerStrategy(RolloutStrategy):
             finally:
                 logger.info("DAgger corrections-only loop ended — pausing engine")
                 engine.pause()
-                # TODO(Steven): re-enable once Teleoperator motor-control methods
-                # are standardised across all teleop implementations.
-                # teleop.disable_torque()
                 with contextlib.suppress(Exception):
                     with self._episode_lock:
                         dataset.save_episode()
@@ -698,36 +728,71 @@ class DAggerStrategy(RolloutStrategy):
         new_phase: DAggerPhase,
         engine,
         interpolator,
-        robot: ThreadSafeRobot,
-        teleop: Teleoperator,
+        ctx: RolloutContext,
+        prev_action: dict | None,
     ) -> None:
-        """Execute side-effects for a validated phase transition."""
+        """Execute side-effects for a validated phase transition, including smooth handovers.
+
+        AUTONOMOUS -> PAUSED (actuated teleop):
+            Pause the engine, then drive the leader arm to the follower's last
+            commanded position so the operator takes over without a jerk.
+
+        PAUSED -> CORRECTING (non-actuated teleop):
+            Slide the follower to the teleop's current pose so the robot meets
+            the operator's hand rather than jumping to it on the first frame.
+
+        CORRECTING -> PAUSED (actuated teleop):
+            Re-enable torque to hold position after correction.
+            This will be potentially useful if cancelling the correction recording
+
+        PAUSED -> AUTONOMOUS:
+            Reset and resume the inference engine.
+        """
+        teleop = ctx.hardware.teleop
+        robot = ctx.hardware.robot_wrapper
+
         logger.info("Phase transition: %s -> %s", old_phase.value, new_phase.value)
         if old_phase == DAggerPhase.AUTONOMOUS and new_phase == DAggerPhase.PAUSED:
-            logger.info("Pausing engine — robot holds position")
+            logger.info("Pausing engine - robot holds position")
             engine.pause()
-            obs = robot.get_observation()
-            _robot_pos = {
-                k: v for k, v in obs.items() if k.endswith(".pos") and k in robot.observation_features
-            }
-            # TODO(Steven): once Teleoperator motor-control methods are
-            # standardised, drive the leader to the follower's pose here so the
-            # operator does not need to pre-align the arm by hand.  Until then
-            # the user is responsible for the alignment.
-            # _teleop_smooth_move_to(teleop, _robot_pos, duration_s=2.0, fps=50)
 
-        elif new_phase == DAggerPhase.CORRECTING:
-            logger.info("Entering correction mode — human teleop control")
-            # TODO(Steven): re-enable once Teleoperator motor-control methods
-            # are standardised across all teleop implementations.
-            # teleop.disable_torque()
+            if _teleop_supports_feedback(teleop) and prev_action is not None:
+                # TODO(Maxime): prev_action is in robot action key space (output of robot_action_processor).
+                # send_feedback expects teleop feedback key space. For homogeneous setups (e.g. SO-101
+                # leader + SO-101 follower) the keys are identical so this works. If the processor pipeline
+                # does non-trivial key renaming (e.g. a rename_map on action keys), the interpolation in
+                # _teleop_smooth_move_to silently no-ops and the arm doesn't move.
+                logger.info("Smooth handover: moving leader arm to follower position")
+                _teleop_smooth_move_to(teleop, prev_action)
+
+        elif old_phase == DAggerPhase.PAUSED and new_phase == DAggerPhase.CORRECTING:
+            logger.info("Entering correction mode - human teleop control")
+            if not _teleop_supports_feedback(teleop) and prev_action is not None:
+                logger.info("Smooth handover: sliding follower to teleop position")
+                obs = robot.get_observation()
+                teleop_action = teleop.get_action()
+                processed = ctx.processors.teleop_action_processor((teleop_action, obs))
+                target = ctx.processors.robot_action_processor((processed, obs))
+                _follower_smooth_move_to(robot, prev_action, target)
+
+            # unlock the teleop for human control
+            if _teleop_supports_feedback(teleop):
+                teleop.disable_torque()
+
+        elif old_phase == DAggerPhase.CORRECTING and new_phase == DAggerPhase.PAUSED:
+            if _teleop_supports_feedback(teleop):
+                teleop.enable_torque()
 
         elif new_phase == DAggerPhase.AUTONOMOUS:
-            logger.info("Resuming autonomous mode — resetting engine and interpolator")
+            logger.info("Resuming autonomous mode - resetting engine and interpolator")
             interpolator.reset()
             engine.reset()
             engine.resume()
 
+            # release teleop before resuming the policy
+            if _teleop_supports_feedback(teleop):
+                teleop.disable_torque()
+
     # ------------------------------------------------------------------
     # Background push (shared by both modes)
     # ------------------------------------------------------------------

src/lerobot/scripts/lerobot_train.py

@@ -277,9 +277,14 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
     if cfg.peft is not None:
         if cfg.is_reward_model_training:
             raise ValueError("PEFT is only supported for policy training. ")
-        logging.info("Using PEFT! Wrapping model.")
-        peft_cli_overrides = dataclasses.asdict(cfg.peft)
-        policy = policy.wrap_with_peft(peft_cli_overrides=peft_cli_overrides)
+        from peft import PeftModel
+
+        if isinstance(policy, PeftModel):
+            logging.info("PEFT adapter already loaded from checkpoint, skipping wrap_with_peft.")
+        else:
+            logging.info("Using PEFT! Wrapping model.")
+            peft_cli_overrides = dataclasses.asdict(cfg.peft)
+            policy = policy.wrap_with_peft(peft_cli_overrides=peft_cli_overrides)
 
     # Wait for all processes to finish model creation before continuing
     accelerator.wait_for_everyone()

src/lerobot/teleoperators/openarm_mini/openarm_mini.py

@@ -112,7 +112,7 @@ class OpenArmMini(Teleoperator):
 
     @property
     def feedback_features(self) -> dict[str, type]:
-        return {}
+        return self.action_features
 
     @property
     def is_connected(self) -> bool:
@@ -348,8 +348,9 @@ class OpenArmMini(Teleoperator):
         if left_goals:
             self.bus_left.sync_write("Goal_Position", left_goals)
 
+    @check_if_not_connected
     def send_feedback(self, feedback: dict[str, float]) -> None:
-        raise NotImplementedError("Feedback is not yet implemented for OpenArm Mini.")
+        self.write_goal_positions(feedback)
 
     @check_if_not_connected
     def disconnect(self) -> None:

src/lerobot/teleoperators/so_leader/so_leader.py

@@ -59,7 +59,7 @@ class SOLeader(Teleoperator):
 
     @property
     def feedback_features(self) -> dict[str, type]:
-        return {}
+        return self.action_features
 
     @property
     def is_connected(self) -> bool:
@@ -130,6 +130,12 @@ class SOLeader(Teleoperator):
         for motor in self.bus.motors:
             self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
 
+    def enable_torque(self) -> None:
+        self.bus.enable_torque()
+
+    def disable_torque(self) -> None:
+        self.bus.disable_torque()
+
     def setup_motors(self) -> None:
         for motor in reversed(self.bus.motors):
             input(f"Connect the controller board to the '{motor}' motor only and press enter.")
@@ -145,9 +151,11 @@ class SOLeader(Teleoperator):
         logger.debug(f"{self} read action: {dt_ms:.1f}ms")
         return action
 
+    @check_if_not_connected
     def send_feedback(self, feedback: dict[str, float]) -> None:
-        # TODO: Implement force feedback
-        raise NotImplementedError
+        goals = {k.removesuffix(".pos"): v for k, v in feedback.items() if k.endswith(".pos")}
+        if goals:
+            self.bus.sync_write("Goal_Position", goals)
 
     @check_if_not_connected
     def disconnect(self) -> None:

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.15' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
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+version = "1.5.4"
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+    { url = "https://files.pythonhosted.org/packages/11/d0/c177e29701cf1d3008d7d2b16b5fc626592ce13bd535f8795c5f57187e0e/cuda_pathfinder-1.5.4-py3-none-any.whl", hash = "sha256:9563d3175ce1828531acf4b94e1c1c7d67208c347ca002493e2654878b26f4b7", size = 51657, upload-time = "2026-04-27T22:42:07.712Z" },
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+version = "4.8.5"
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+    { name = "transformers", marker = "extra == 'transformers-dep'", specifier = ">=5.4.0,<5.6.0" },
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