Files
lerobot-clone/src/lerobot/utils/constants.py
Khalil Meftah e963e5a0c4 RL stack refactoring (#3075)
* refactor: RL stack refactoring — RLAlgorithm, RLTrainer, DataMixer, and SAC restructuring

* chore: clarify torch.compile disabled note in SACAlgorithm

* fix(teleop): keyboard EE teleop not registering special keys and losing intervention state

Fixes #2345

Co-authored-by: jpizarrom <jpizarrom@gmail.com>

* fix: remove leftover normalization calls from reward classifier predict_reward

Fixes #2355

* fix: add thread synchronization to ReplayBuffer to prevent race condition between add() and sample()

* refactor: update SACAlgorithm to pass action_dim to _init_critics and fix encoder reference

* perf: remove redundant CPU→GPU→CPU transition move in learner

* Fix: add kwargs in reward classifier __init__()

* fix: include IS_INTERVENTION in complementary_info sent to learner for offline replay buffer

* fix: add try/finally to control_loop to ensure image writer cleanup on exit

* fix: use string key for IS_INTERVENTION in complementary_info to avoid torch.load serialization error

* fix: skip tests that require grpc if not available

* fix(tests): ensure tensor stats comparison accounts for reshaping in normalization tests

* fix(tests): skip tests that require grpc if not available

* refactor(rl): expose public API in rl/__init__ and use relative imports in sub-packages

* fix(config): update vision encoder model name to lerobot/resnet10

* fix(sac): clarify torch.compile status

* refactor(rl): update shutdown_event type hints from 'any' to 'Any' for consistency and clarity

* refactor(sac): simplify optimizer return structure

* perf(rl): use async iterators in OnlineOfflineMixer.get_iterator

* refactor(sac): decouple algorithm hyperparameters from policy config

* update losses names in tests

* fix docstring

* remove unused type alias

* fix test for flat dict structure

* refactor(policies): rename policies/sac → policies/gaussian_actor

* refactor(rl/sac): consolidate hyperparameter ownership and clean up discrete critic

* perf(observation_processor): add CUDA support for image processing

* fix(rl): correctly wire HIL-SERL gripper penalty through processor pipeline

(cherry picked from commit 9c2af818ff)

* fix(rl): add time limit processor to environment pipeline

(cherry picked from commit cd105f65cb)

* fix(rl): clarify discrete gripper action mapping in GripperVelocityToJoint for SO100

(cherry picked from commit 494f469a2b)

* fix(rl): update neutral gripper action

(cherry picked from commit 9c9064e5be)

* fix(rl): merge environment and action-processor info in transition processing

(cherry picked from commit 30e1886b64)

* fix(rl): mirror gym_manipulator in actor

(cherry picked from commit d2a046dfc5)

* fix(rl): postprocess action in actor

(cherry picked from commit c2556439e5)

* fix(rl): improve action processing for discrete and continuous actions

(cherry picked from commit f887ab3f6a)

* fix(rl): enhance intervention handling in actor and learner

(cherry picked from commit ef8bfffbd7)

* Revert "perf(observation_processor): add CUDA support for image processing"

This reverts commit 38b88c414c.

* refactor(rl): make algorithm a nested config so all SAC hyperparameters are JSON-addressable

* refactor(rl): add make_algorithm_config function for RLAlgorithmConfig instantiation

* refactor(rl): add type property to RLAlgorithmConfig for better clarity

* refactor(rl): make RLAlgorithmConfig an abstract base class for better extensibility

* refactor(tests): remove grpc import checks from test files for cleaner code

* fix(tests): gate RL tests on the `datasets` extra

* refactor: simplify docstrings for clarity and conciseness across multiple files

* fix(rl): update gripper position key and handle action absence during reset

* fix(rl): record pre-step observation so (obs, action, next.reward) align in gym_manipulator dataset

* refactor: clean up import statements

* chore: address reviewer comments

* chore: improve visual stats reshaping logic and update docstring for clarity

* refactor: enforce mandatory config_class and name attributes in RLAlgorithm

* refactor: implement NotImplementedError for abstract methods in RLAlgorithm and DataMixer

* refactor: replace build_algorithm with make_algorithm for SACAlgorithmConfig and update related tests

* refactor: add require_package calls for grpcio and gym-hil in relevant modules

* refactor(rl): move grpcio guards to runtime entry points

* feat(rl): consolidate HIL-SERL checkpoint into HF-style components

Make `RLAlgorithmConfig` and `RLAlgorithm` `HubMixin`s, add abstract
`state_dict()` / `load_state_dict()` for critic ensemble, target nets
and `log_alpha`, and persist them as a sibling `algorithm/` component
next to `pretrained_model/`. Replace the pickled `training_state.pt`
with an enriched `training_step.json` carrying `step` and
`interaction_step`, so resume restores actor + critics + target nets +
temperature + optimizers + RNG + counters from HF-standard files.

* refactor(rl): move actor weight-sync wire format from policy to algorithm

* refactor(rl): update type hints for learner and actor functions

* refactor(rl): hoist grpcio guard to module top in actor/learner

* chore(rl): manage import pattern in actor (#3564)

* chore(rl): manage import pattern in actor

* chore(rl): optional grpc imports in learner; quote grpc ServicerContext types

---------

Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>

* update uv.lock

* chore(doc): update doc

---------

Co-authored-by: jpizarrom <jpizarrom@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
2026-05-12 15:49:54 +02:00

112 lines
4.1 KiB
Python

# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# keys
import os
from pathlib import Path
from huggingface_hub.constants import HF_HOME
OBS_STR = "observation"
OBS_PREFIX = OBS_STR + "."
OBS_ENV_STATE = OBS_STR + ".environment_state"
OBS_STATE = OBS_STR + ".state"
OBS_IMAGE = OBS_STR + ".image"
OBS_IMAGES = OBS_IMAGE + "s"
OBS_LANGUAGE = OBS_STR + ".language"
OBS_LANGUAGE_TOKENS = OBS_LANGUAGE + ".tokens"
OBS_LANGUAGE_ATTENTION_MASK = OBS_LANGUAGE + ".attention_mask"
OBS_LANGUAGE_SUBTASK = OBS_STR + ".subtask"
OBS_LANGUAGE_SUBTASK_TOKENS = OBS_LANGUAGE_SUBTASK + ".tokens"
OBS_LANGUAGE_SUBTASK_ATTENTION_MASK = OBS_LANGUAGE_SUBTASK + ".attention_mask"
ACTION = "action"
ACTION_PREFIX = ACTION + "."
ACTION_TOKENS = ACTION + ".tokens"
ACTION_TOKEN_MASK = ACTION + ".token_mask"
REWARD = "next.reward"
TRUNCATED = "next.truncated"
DONE = "next.done"
INFO = "info"
ROBOTS = "robots"
TELEOPERATORS = "teleoperators"
# files & directories
CHECKPOINTS_DIR = "checkpoints"
LAST_CHECKPOINT_LINK = "last"
PRETRAINED_MODEL_DIR = "pretrained_model"
TRAINING_STATE_DIR = "training_state"
ALGORITHM_DIR = "algorithm"
RNG_STATE = "rng_state.safetensors"
TRAINING_STEP = "training_step.json"
OPTIMIZER_STATE = "optimizer_state.safetensors"
OPTIMIZER_PARAM_GROUPS = "optimizer_param_groups.json"
SCHEDULER_STATE = "scheduler_state.json"
POLICY_PREPROCESSOR_DEFAULT_NAME = "policy_preprocessor"
POLICY_POSTPROCESSOR_DEFAULT_NAME = "policy_postprocessor"
if "LEROBOT_HOME" in os.environ:
raise ValueError(
f"You have a 'LEROBOT_HOME' environment variable set to '{os.getenv('LEROBOT_HOME')}'.\n"
"'LEROBOT_HOME' is deprecated, please use 'HF_LEROBOT_HOME' instead."
)
# cache dir
default_cache_path = Path(HF_HOME) / "lerobot"
HF_LEROBOT_HOME = Path(os.getenv("HF_LEROBOT_HOME", default_cache_path)).expanduser()
# LeRobot's own revision-safe Hub cache (NOT the system-wide ~/.cache/huggingface/hub/).
# Used as the ``cache_dir`` argument to ``snapshot_download`` so that different
# dataset revisions are stored in isolated snapshot directories.
HF_LEROBOT_HUB_CACHE = HF_LEROBOT_HOME / "hub"
# calibration dir
default_calibration_path = HF_LEROBOT_HOME / "calibration"
HF_LEROBOT_CALIBRATION = Path(os.getenv("HF_LEROBOT_CALIBRATION", default_calibration_path)).expanduser()
# Dataset meta-features (auto-populated by the recording pipeline)
DEFAULT_FEATURES = {
"timestamp": {"dtype": "float32", "shape": (1,), "names": None},
"frame_index": {"dtype": "int64", "shape": (1,), "names": None},
"episode_index": {"dtype": "int64", "shape": (1,), "names": None},
"index": {"dtype": "int64", "shape": (1,), "names": None},
"task_index": {"dtype": "int64", "shape": (1,), "names": None},
}
# ImageNet normalization constants
IMAGENET_STATS = {
"mean": [[[0.485]], [[0.456]], [[0.406]]], # (c,1,1)
"std": [[[0.229]], [[0.224]], [[0.225]]], # (c,1,1)
}
# streaming datasets
LOOKBACK_BACKTRACKTABLE = 100
LOOKAHEAD_BACKTRACKTABLE = 100
# openpi
OPENPI_ATTENTION_MASK_VALUE = -2.3819763e38 # TODO(pepijn): Modify this when extending support to fp8 models
# Constants for LIBERO observation keys
LIBERO_KEY_EEF_POS = "robot_state/eef/pos"
LIBERO_KEY_EEF_QUAT = "robot_state/eef/quat"
LIBERO_KEY_EEF_MAT = "robot_state/eef/mat"
LIBERO_KEY_EEF_AXISANGLE = "robot_state/eef/axisangle"
LIBERO_KEY_GRIPPER_QPOS = "robot_state/gripper/qpos"
LIBERO_KEY_GRIPPER_QVEL = "robot_state/gripper/qvel"
LIBERO_KEY_JOINTS_POS = "robot_state/joints/pos"
LIBERO_KEY_JOINTS_VEL = "robot_state/joints/vel"
LIBERO_KEY_PIXELS_AGENTVIEW = "pixels/agentview_image"
LIBERO_KEY_PIXELS_EYE_IN_HAND = "pixels/robot0_eye_in_hand_image"