chore(deps): bump the uv group across 1 directory with 2 updates

Bumps the uv group with 2 updates in the / directory: [setuptools](https://github.com/pypa/setuptools) and [pytest](https://github.com/pytest-dev/pytest).


Updates `setuptools` from 80.10.2 to 83.0.0
- [Release notes](https://github.com/pypa/setuptools/releases)
- [Changelog](https://github.com/pypa/setuptools/blob/main/NEWS.rst)
- [Commits](https://github.com/pypa/setuptools/compare/v80.10.2...v83.0.0)

Updates `pytest` from 8.4.2 to 9.0.3
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.4.2...9.0.3)

---
updated-dependencies:
- dependency-name: setuptools
  dependency-version: 83.0.0
  dependency-type: direct:development
  dependency-group: uv
- dependency-name: pytest
  dependency-version: 9.0.3
  dependency-type: direct:production
  dependency-group: uv
...

Signed-off-by: dependabot[bot] <support@github.com>

AGENT_GUIDE.md

@@ -61,20 +61,16 @@ Full details in [`docs/source/so101.mdx`](./docs/source/so101.mdx) and [`docs/so
 **4.1 Install**
 
 ```bash
-# uv (recommended — see AGENTS.md and CLAUDE.md)
-uv sync --locked --extra feetech          # SO-100/SO-101 motor stack
-# uv sync --locked --extra all            # everything
-# uv sync --locked --extra smolvla        # add SmolVLA deps
-
-# pip (alternative, e.g. when not working from source)
-# pip install 'lerobot[feetech]'
-# pip install 'lerobot[all]'
-# pip install 'lerobot[smolvla]'
-
+pip install 'lerobot[feetech]'              # SO-100/SO-101 motor stack
+# pip install 'lerobot[all]'                # everything
+# pip install 'lerobot[aloha,pusht]'        # specific features
+# pip install 'lerobot[smolvla]'            # add SmolVLA deps
 git lfs install && git lfs pull
-hf auth login                             # required to push datasets/policies
+hf auth login                               # required to push datasets/policies
 ```
 
+Contributors can alternatively use `uv sync --locked --extra feetech` (see `AGENTS.md`).
+
 **4.2 Find USB ports** — run once per arm, unplug when prompted.
 
 ```bash

docker/Dockerfile.internal

@@ -68,16 +68,17 @@ ENV HOME=/home/user_lerobot \
 # issues with MuJoCo and OpenGL drivers.
 RUN uv venv --python python${PYTHON_VERSION}
 
-# Install third-party dependencies separately for layer caching
+# Install Python dependencies for caching
 COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
-RUN uv sync --locked --extra all --no-install-project --no-cache
+COPY --chown=user_lerobot:user_lerobot src/ src/
+
+RUN uv sync --locked --extra all --no-cache
 
 RUN chmod +x /lerobot/.venv/lib/python${PYTHON_VERSION}/site-packages/triton/backends/nvidia/bin/ptxas
 
-# Copy the application source code and install the local project
+# Copy the rest of the application source code
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .
-RUN uv sync --locked --extra all --no-cache
 
 # Set the default command
 CMD ["/bin/bash"]

docker/Dockerfile.user

@@ -60,14 +60,15 @@ ENV HOME=/home/user_lerobot \
 # run other Python projects in the same container without dependency conflicts.
 RUN uv venv
 
-# Install third-party dependencies separately for layer caching
+# Install Python dependencies for caching
 COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
-RUN uv sync --locked --extra all --no-install-project --no-cache
+COPY --chown=user_lerobot:user_lerobot src/ src/
 
-# Copy the application code and install the local project
+RUN uv sync --locked --extra all --no-cache
+
+# Copy the rest of the application code
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .
-RUN uv sync --locked --extra all --no-cache
 
 # Set the default command
 CMD ["/bin/bash"]

docs/source/backwardcomp.mdx

@@ -58,7 +58,7 @@ final_action = postprocessor(action)
 
 ## Hardware API redesign
 
-PR [#777](https://github.com/huggingface/lerobot/pull/777) improves the LeRobot calibration but is **not backward-compatible**. Below is an overview of what changed and how you can continue to work with datasets created before this pull request.
+PR [#777](https://github.com/huggingface/lerobot/pull/777) improves the LeRobot calibration but is **not backward-compatible**. Below is a overview of what changed and how you can continue to work with datasets created before this pull request.
 
 ### What changed?
 
@@ -129,8 +129,8 @@ python examples/backward_compatibility/replay.py \
 
 Policies output actions in the same format as the datasets (`torch.Tensors`). Therefore, the same transformations should be applied.
 
-To find these transformations, we recommend first replaying an episode of the dataset your policy was trained on using the section above.
-Then, add these same transformations to your inference script (shown here in the `record.py` script):
+To find these transformations, we recommend to first try and and replay an episode of the dataset your policy was trained on using the section above.
+Then, add these same transformations on your inference script (shown here in the `record.py` script):
 
 ```diff
 action_values = predict_action(

docs/source/env_processor.mdx

@@ -88,6 +88,20 @@ policy_preprocessor = NormalizerProcessorStep(stats=dataset_stats)
 
 The same policy can work with different environment processors, and the same environment processor can work with different policies:
 
+````python
+# Use SmolVLA policy with LIBERO environment
+# Use SmolVLA policy with LIBERO environment
+libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
+    env_cfg=libero_cfg,
+    policy_cfg=smolvla_cfg,
+)
+smolvla_preprocessor, smolvla_postprocessor = make_pre_post_processors(smolvla_cfg)
+# Or use ACT policy with the same LIBERO environment
+libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
+    env_cfg=libero_cfg,
+    policy_cfg=act_cfg,
+)
+act_preprocessor, act_postprocessor = make_pre_post_processors(act_cfg)
 ```python
 # Use SmolVLA policy with LIBERO environment
 libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
@@ -102,7 +116,6 @@ libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
     policy_cfg=act_cfg,
 )
 act_preprocessor, act_postprocessor = make_pre_post_processors(act_cfg)
-```
 
 ### 3. **Easier Experimentation**
 
@@ -132,7 +145,7 @@ class LiberoVelocityProcessorStep(ObservationProcessorStep):
         state = torch.cat([eef_pos, eef_axisangle, eef_vel,
                           gripper_pos, gripper_vel], dim=-1)  # 14D
         return state
-```
+````
 
 ### 4. **Cleaner Environment Code**
 

docs/source/feetech.mdx

@@ -40,10 +40,10 @@ This tutorial guides you through updating the firmware of Feetech motors using t
 For each motor you want to update:
 
 1. **Select the motor** from the list by clicking on it
-2. **Click the Upgrade tab**:
-3. **Click the Online button**:
-   - If a potential firmware update is found, it will be displayed in the box
-4. **Click the Upgrade button**:
+2. **Click on Upgrade tab**:
+3. **Click on Online button**:
+   - If an potential firmware update is found, it will be displayed in the box
+4. **Click on Upgrade button**:
    - The update progress will be displayed
 
 ## Step 6: Verify Update

docs/source/hil_data_collection.mdx

@@ -59,7 +59,6 @@ The `lerobot-rollout --strategy.type=dagger` mode requires **teleoperators with
 
 - `bi_openarm_mini` - Bimanual OpenArm Mini
 - `so_leader` - SO100 / SO101 leader arm
-- `bi_so_leader` - Bimanual SO100 / SO101 leader arms
 
 > [!IMPORTANT]
 > The provided commands default to `bi_openarm_follower` + `bi_openarm_mini`.

docs/source/hope_jr.mdx

@@ -211,7 +211,7 @@ Record, Replay and Train with Hope-JR is still experimental.
 
 ### Record
 
-This step records the dataset, which can be seen as an example [here](https://huggingface.co/datasets/nepyope/hand_record_test_with_video_data).
+This step records the dataset, which can be seen as an example [here](https://huggingface.co/datasets/nepyope/hand_record_test_with_video_data/settings).
 
 ```bash
 lerobot-record \

docs/source/integrate_hardware.mdx

@@ -18,7 +18,7 @@ If you're using Feetech or Dynamixel motors, LeRobot provides built-in bus inter
 - [`DynamixelMotorsBus`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/motors/dynamixel/dynamixel.py) – for controlling Dynamixel servos
 
 Please refer to the [`MotorsBus`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/motors/motors_bus.py) abstract class to learn about its API.
-For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/robots/so_follower/so_follower.py)
+For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/robots/so_follower/so101_follower/so101_follower.py)
 
 Use these if compatible. Otherwise, you'll need to find or write a Python interface (not covered in this tutorial):
 

docs/source/lekiwi.mdx

@@ -51,7 +51,7 @@ In addition to these instructions, you need to install the Feetech SDK & ZeroMQ
 pip install -e ".[lekiwi]"
 ```
 
-Great 🤗! You are now done installing LeRobot, and we can begin assembling the SO100/SO101 arms and the mobile base 🤖.
+Great :hugs:! You are now done installing LeRobot, and we can begin assembling the SO100/SO101 arms and the mobile base :robot:.
 Every time you now want to use LeRobot, you can go to the `~/lerobot` folder where we installed LeRobot and run one of the commands.
 
 # Step-by-Step Assembly Instructions

docs/source/pi0fast.mdx

@@ -174,7 +174,7 @@ The model takes images, text instructions, and robot state as input, and outputs
 
 ## Reproducing π₀Fast results
 
-We reproduce the results of π₀Fast on the LIBERO benchmark using the LeRobot implementation. We take the LeRobot PiFast base model [lerobot/pi0fast-base](https://huggingface.co/lerobot/pi0fast-base) and finetune for an additional 40k steps in bfloat16, with batch size of 256 on 8 H100 GPUs using the [HuggingFace LIBERO dataset](https://huggingface.co/datasets/HuggingFaceVLA/libero).
+We reproduce the results of π₀Fast on the LIBERO benchmark using the LeRobot implementation. We take the LeRobot PiFast base model [lerobot/pi0fast-base](https://huggingface.co/lerobot/pi0fast-base) and finetune for an additional 40kk steps in bfloat16, with batch size of 256 on 8 H100 GPUs using the [HuggingFace LIBERO dataset](https://huggingface.co/datasets/HuggingFaceVLA/libero).
 
 The finetuned model can be found here:
 

docs/source/processors_robots_teleop.mdx

@@ -22,7 +22,7 @@ With processors, you choose the learning features you want to use for your polic
 ## Three pipelines
 
 We often compose three pipelines. Depending on your setup, some can be empty if action and observation spaces already match.
-Each of these pipelines handles different conversions between different action and observation spaces. Below is a quick explanation of each pipeline.
+Each of these pipelines handle different conversions between different action and observation spaces. Below is a quick explanation of each pipeline.
 
 1. Pipeline 1: Teleop action space → dataset action space (phone pose → EE targets)
 2. Pipeline 2: Dataset action space → robot command space (EE targets → joints)
@@ -74,15 +74,15 @@ In the phone to SO-100 follower examples we use the following adapters:
 - `robot_action_to_transition`: transforms the teleop action dict to a pipeline transition.
 - `transition_to_robot_action`: transforms the pipeline transition to a robot action dict.
 - `observation_to_transition`: transforms the robot observation dict to a pipeline transition.
-- `transition_to_observation`: transforms the pipeline transition to an observation dict.
+- `transition_to_observation`: transforms the pipeline transition to a observation dict.
 
-Check out [src/lerobot/processor/converters.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/converters.py) for more details.
+Checkout [src/lerobot/processor/converters.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/converters.py) for more details.
 
 ## Dataset feature contracts
 
 Dataset features are determined by the keys saved in the dataset. Each step can declare what features it modifies in a contract called `transform_features(...)`. Once you build a processor, the processor can then aggregate all of these features with `aggregate_pipeline_dataset_features()` and merge multiple feature dicts with `combine_feature_dicts(...)`.
 
-Below is an example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples:
+Below is and example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples:
 
 ```python
     def transform_features(

docs/source/robocasa.mdx

@@ -82,8 +82,6 @@ By default the env samples objects only from the `lightwheel` registry (what `--
 
 All eval snippets below mirror the CI command (see `.github/workflows/benchmark_tests.yml`). The `--rename_map` argument maps RoboCasa's native camera keys (`robot0_agentview_left` / `robot0_eye_in_hand` / `robot0_agentview_right`) onto the three-camera (`camera1` / `camera2` / `camera3`) input layout the released `smolvla_robocasa` policy was trained on.
 
-By default, each task uses the rollout horizon registered by RoboCasa. Set `--env.episode_length=<steps>` to apply the same explicit horizon to every selected task.
-
 ### Single-task evaluation (recommended for quick iteration)
 
 ```bash

docs/source/rtc.mdx

@@ -57,7 +57,7 @@ policy_cfg.rtc_config = RTCConfig(
 policy = PI0Policy.from_pretrained("lerobot/pi0_base", policy_cfg=policy_cfg, device="cuda")
 
 # Now use predict_action_chunk with RTC parameters
-inference_delay = 4  # How many steps of inference latency, this value should be calculated based on the inference latency of the policy
+inference_delay = 4  # How many steps of inference latency, this values should be calculated based on the inference latency of the policy
 
 # Initialize the action queue
 action_queue = ActionQueue(policy_cfg.rtc_config)
@@ -100,7 +100,7 @@ Typical values: 8-12 steps
 RTCConfig(execution_horizon=10)
 ```
 
-**`max_guidance_weight`**: How strongly to enforce consistency with the previous chunk. This is a hyperparameter that can be tuned to balance the smoothness of the transitions and the reactivity of the policy. For 10 steps flow matching (SmolVLA, Pi0, Pi0.5), a value of 10.0 is an optimal value.
+**`max_guidance_weight`**: How strongly to enforce consistency with the previous chunk. This is a hyperparameter that can be tuned to balance the smoothness of the transitions and the reactivity of the policy. For 10 steps flow matching (SmolVLA, Pi0, Pi0.5), a value of 10.0 is a optimal value.
 
 **`prefix_attention_schedule`**: How to weight consistency across the overlap region.
 

docs/source/smolvla.mdx

@@ -93,7 +93,7 @@ lerobot-train --help
 
 ## Evaluate the finetuned model and run it in real-time
 
-Similarly for when recording an episode, it is recommended that you are logged in to the HuggingFace Hub. You can follow the corresponding steps: [Record a dataset](./il_robots#record-a-dataset).
+Similarly for when recording an episode, it is recommended that you are logged in to the HuggingFace Hub. You can follow the corresponding steps: [Record a dataset](./il_robots).
 Once you are logged in, you can run inference in your setup by doing:
 
 ```bash

docs/source/so101.mdx

@@ -338,7 +338,7 @@ It is advisable to install one 3-pin cable in the motor after placing them befor
 <hfoption id="Leader">
 
 - Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
-- Attach the handle to the leader holder using 1 M2x6mm screw.
+- Attach the handle to motor 5 using 1 M2x6mm screw.
 - Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
 - Attach the follower trigger with 4 M3x6mm screws.
 

docs/source/using_dataset_tools.mdx

@@ -50,11 +50,11 @@ lerobot-edit-dataset \
 Divide a dataset into multiple subsets.
 
 ```bash
-# Split by fractions (e.g. 60% train, 20% val, 20% test)
+# Split by fractions (e.g. 80% train, 20% test, 20% val)
 lerobot-edit-dataset \
     --repo_id lerobot/pusht \
     --operation.type split \
-    --operation.splits '{"train": 0.6, "val": 0.2, "test": 0.2}'
+    --operation.splits '{"train": 0.8, "test": 0.2, "val": 0.2}'
 
 # Split by specific episode indices
 lerobot-edit-dataset \

examples/isaac_teleop_to_so101/common.py

@@ -44,7 +44,6 @@ from typing import Protocol
 
 import numpy as np
 
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
     RobotProcessorPipeline,
@@ -57,6 +56,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
     EEBoundsAndSafety,
     InverseKinematicsEEToJoints,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import HF_LEROBOT_CALIBRATION, HF_LEROBOT_HOME, TELEOPERATORS
 from lerobot.utils.robot_utils import precise_sleep
 

examples/isaac_teleop_to_so101/isaac_teleop/teleop_so101_leader_arm.py

@@ -38,7 +38,7 @@ from typing import TYPE_CHECKING
 
 import numpy as np
 
-from lerobot.lerobot_types import RobotAction
+from lerobot.types import RobotAction
 
 from .base import _GRIPPER_MOTOR_SCALE, IsaacTeleopTeleoperator, _isaacteleop_available
 from .config_isaac_teleop import SO101LeaderArmConfig

examples/isaac_teleop_to_so101/isaac_teleop/teleop_xr_controller.py

@@ -32,7 +32,7 @@ from typing import TYPE_CHECKING, Any
 
 import numpy as np
 
-from lerobot.lerobot_types import RobotAction
+from lerobot.types import RobotAction
 
 from .base import IsaacTeleopTeleoperator, _isaacteleop_available
 from .config_isaac_teleop import XRControllerConfig

examples/isaac_teleop_to_so101/isaac_teleop/xr_controller_processor.py

@@ -26,8 +26,8 @@ from __future__ import annotations
 from dataclasses import dataclass
 
 from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import RobotAction
 from lerobot.processor import ProcessorStepRegistry, RobotActionProcessorStep
+from lerobot.types import RobotAction
 from lerobot.utils.rotation import Rotation
 
 from .base import _GRIPPER_MOTOR_SCALE

examples/phone_to_so100/evaluate.py

@@ -21,7 +21,6 @@ from lerobot.cameras.opencv import OpenCVCameraConfig
 from lerobot.common.control_utils import predict_action
 from lerobot.configs import FeatureType, PolicyFeature
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.policies import make_pre_post_processors
 from lerobot.policies.act import ACTPolicy
@@ -39,6 +38,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
     ForwardKinematicsJointsToEE,
     InverseKinematicsEEToJoints,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
 from lerobot.utils.keyboard_input import init_keyboard_listener

examples/phone_to_so100/record.py

@@ -16,7 +16,6 @@
 
 from lerobot.cameras.opencv import OpenCVCameraConfig
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
     RobotProcessorPipeline,
@@ -37,6 +36,7 @@ from lerobot.scripts.lerobot_record import record_loop
 from lerobot.teleoperators.phone import Phone, PhoneConfig
 from lerobot.teleoperators.phone.config_phone import PhoneOS
 from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.feature_utils import combine_feature_dicts
 from lerobot.utils.keyboard_input import init_keyboard_listener
 from lerobot.utils.utils import log_say

examples/phone_to_so100/replay.py

@@ -17,7 +17,6 @@
 import time
 
 from lerobot.datasets import LeRobotDataset
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
     RobotProcessorPipeline,
@@ -28,6 +27,7 @@ from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
 from lerobot.robots.so_follower.robot_kinematic_processor import (
     InverseKinematicsEEToJoints,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say

examples/phone_to_so100/rollout.py

@@ -27,7 +27,6 @@ Highlight, or DAgger via ``lerobot-rollout --strategy.type=...``.
 
 from lerobot.cameras.opencv import OpenCVCameraConfig
 from lerobot.configs import PreTrainedConfig
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
     RobotProcessorPipeline,
@@ -44,6 +43,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
 from lerobot.rollout.inference import SyncInferenceConfig
 from lerobot.rollout.strategies import BaseStrategy
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.utils import init_logging
 

examples/phone_to_so100/teleoperate.py

@@ -15,7 +15,6 @@
 
 import time
 
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
     RobotProcessorPipeline,
@@ -32,6 +31,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 from lerobot.teleoperators.phone import Phone, PhoneConfig
 from lerobot.teleoperators.phone.config_phone import PhoneOS
 from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 

examples/so100_to_so100_EE/evaluate.py

@@ -21,7 +21,6 @@ from lerobot.cameras.opencv import OpenCVCameraConfig
 from lerobot.common.control_utils import predict_action
 from lerobot.configs import FeatureType, PolicyFeature
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.policies import make_pre_post_processors
 from lerobot.policies.act import ACTPolicy
@@ -39,6 +38,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
     ForwardKinematicsJointsToEE,
     InverseKinematicsEEToJoints,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
 from lerobot.utils.keyboard_input import init_keyboard_listener

examples/so100_to_so100_EE/record.py

@@ -17,7 +17,6 @@
 
 from lerobot.cameras.opencv import OpenCVCameraConfig
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
     RobotProcessorPipeline,
@@ -34,6 +33,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 )
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.feature_utils import combine_feature_dicts
 from lerobot.utils.keyboard_input import init_keyboard_listener
 from lerobot.utils.utils import log_say

examples/so100_to_so100_EE/replay.py

@@ -18,7 +18,6 @@
 import time
 
 from lerobot.datasets import LeRobotDataset
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
     RobotProcessorPipeline,
@@ -29,6 +28,7 @@ from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
 from lerobot.robots.so_follower.robot_kinematic_processor import (
     InverseKinematicsEEToJoints,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say

examples/so100_to_so100_EE/rollout.py

@@ -25,7 +25,6 @@ forward/inverse kinematics.
 
 from lerobot.cameras.opencv import OpenCVCameraConfig
 from lerobot.configs import PreTrainedConfig
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
     RobotProcessorPipeline,
@@ -42,6 +41,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
 from lerobot.rollout.inference import SyncInferenceConfig
 from lerobot.rollout.strategies import BaseStrategy
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.utils import init_logging
 

examples/so100_to_so100_EE/teleoperate.py

@@ -16,7 +16,6 @@
 
 import time
 
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
     RobotProcessorPipeline,
@@ -31,6 +30,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
     InverseKinematicsEEToJoints,
 )
 from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 

pyproject.toml

@@ -14,7 +14,7 @@
 
 [build-system]
 requires = ["setuptools"]
-build-backend = "setuptools.build_meta"
+build-backend = "setuptools*"
 
 [project.urls]
 homepage = "https://huggingface.co/lerobot"
@@ -67,7 +67,7 @@ dependencies = [
     "einops>=0.8.0,<0.9.0",
 
     # Config & Hub
-    "draccus>=0.11.6,<0.12.0",
+    "draccus==0.10.0", # TODO: Relax version constraint
     "huggingface-hub>=1.0.0,<2.0.0",
     "requests>=2.32.0,<3.0.0",
 
@@ -87,7 +87,7 @@ dependencies = [
 
     # Build tools (required by opencv-python-headless on some platforms)
     "cmake>=3.29.0.1,<4.2.0",
-    "setuptools>=71.0.0,<81.0.0",
+    "setuptools>=71.0.0,<84.0.0",
 ]
 
 # Optional dependencies
@@ -261,7 +261,7 @@ annotations = [
 # Development
 dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools>=1.73.1,<2.0.0", "mypy>=1.19.1", "ruff>=0.14.1", "lerobot[notebook]"]
 notebook = ["jupyter>=1.0.0,<2.0.0", "ipykernel>=6.0.0,<7.0.0"]
-test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
+test = ["pytest>=8.1.0,<10.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 
 # Simulation

src/lerobot/async_inference/policy_server.py

@@ -38,7 +38,6 @@ import draccus
 import grpc
 import torch
 
-from lerobot.lerobot_types import PolicyAction
 from lerobot.policies import get_policy_class, make_pre_post_processors
 from lerobot.processor import PolicyProcessorPipeline
 from lerobot.transport import (
@@ -46,6 +45,7 @@ from lerobot.transport import (
     services_pb2_grpc,  # type: ignore
 )
 from lerobot.transport.utils import receive_bytes_in_chunks
+from lerobot.types import PolicyAction
 
 from .configs import PolicyServerConfig
 from .constants import SUPPORTED_POLICIES

src/lerobot/common/control_utils.py

@@ -35,9 +35,9 @@ else:
 
 if TYPE_CHECKING:
     from lerobot.datasets import LeRobotDataset
-from lerobot.lerobot_types import PolicyAction
 from lerobot.processor import PolicyProcessorPipeline
 from lerobot.robots import Robot
+from lerobot.types import PolicyAction
 
 
 def predict_action(

src/lerobot/configs/policies.py

@@ -163,10 +163,8 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
         return None
 
     def _save_pretrained(self, save_directory: Path) -> None:
-        # Encode against the base class so draccus includes the choice "type" key,
-        # which `from_pretrained` needs to resolve the concrete subclass.
-        with open(save_directory / CONFIG_NAME, "w") as f:
-            json.dump(draccus.encode(self, PreTrainedConfig), f, indent=4)
+        with open(save_directory / CONFIG_NAME, "w") as f, draccus.config_type("json"):
+            draccus.dump(self, f, indent=4)
 
     @classmethod
     def from_pretrained(

src/lerobot/configs/rewards.py

@@ -103,10 +103,8 @@ class RewardModelConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
         pass
 
     def _save_pretrained(self, save_directory: Path) -> None:
-        # Encode against the base class so draccus includes the choice "type" key,
-        # which `from_pretrained` needs to resolve the concrete subclass.
-        with open(save_directory / CONFIG_NAME, "w") as f:
-            json.dump(draccus.encode(self, RewardModelConfig), f, indent=4)
+        with open(save_directory / CONFIG_NAME, "w") as f, draccus.config_type("json"):
+            draccus.dump(self, f, indent=4)
 
     @classmethod
     def from_pretrained(

src/lerobot/configs/train.py

@@ -194,11 +194,7 @@ class TrainPipelineConfig(HubMixin):
             )
 
         if Path(config_path).resolve().exists():
-            # `config_path` may point at the checkpoint's train_config.json or at its
-            # pretrained_model/ directory (both documented above) — resolve either to
-            # the pretrained_model/ directory.
-            config_path_obj = Path(config_path)
-            policy_dir = config_path_obj.parent if config_path_obj.is_file() else config_path_obj
+            policy_dir = Path(config_path).parent
             self.checkpoint_path = policy_dir.parent
         elif self.job.is_remote:
             return

src/lerobot/datasets/aggregate.py

@@ -19,7 +19,6 @@ import copy
 import logging
 import shutil
 from pathlib import Path
-from typing import Any, NotRequired, TypedDict
 
 import datasets
 import pandas as pd
@@ -50,32 +49,8 @@ from .utils import (
 )
 from .video_utils import concatenate_video_files, get_video_duration_in_s
 
-logger = logging.getLogger(__name__)
 
-type FeatureDict = dict[str, dict[str, Any]]
-type ChunkFile = tuple[int, int]
-
-
-class IndexState(TypedDict):
-    chunk: int
-    file: int
-    src_to_dst: NotRequired[dict[ChunkFile, ChunkFile]]
-
-
-class VideoIndex(TypedDict):
-    chunk: int
-    file: int
-    latest_duration: float
-    episode_duration: float
-    src_to_offset: NotRequired[dict[ChunkFile, float]]
-    src_to_dst: NotRequired[dict[ChunkFile, ChunkFile]]
-    dst_file_durations: NotRequired[dict[ChunkFile, float]]
-
-
-type VideoIndexState = dict[str, VideoIndex]
-
-
-def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMetadata]) -> FeatureDict:
+def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMetadata]) -> dict[str, dict]:
     """Create a merged video feature info dictionary for aggregation. The video encoder info is merged field-by-field: each key is kept only when every source agrees; otherwise that key is set to ``null`` (or ``{}`` for ``video.extra_options``) and a warning is logged.
 
     Args:
@@ -84,14 +59,14 @@ def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMeta
     Returns:
         dict: A dictionary of merged video feature info.
     """
-    merged_info: FeatureDict = copy.deepcopy(all_metadata[0].features)
+    merged_info = copy.deepcopy(all_metadata[0].features)
     video_keys = [k for k in merged_info if merged_info[k].get("dtype") == "video"]
 
     for vk in video_keys:
         video_infos = [m.features.get(vk, {}).get("info") or {} for m in all_metadata]
         base_video_info = video_infos[0]
 
-        merged_encoder_info: dict[str, Any] = {}
+        merged_encoder_info: dict = {}
         fallback_keys: list[str] = []
         for info_key in VIDEO_ENCODER_INFO_KEYS:
             values = [info.get(info_key, None) for info in video_infos]
@@ -105,7 +80,7 @@ def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMeta
                 merged_encoder_info[info_key] = {} if info_key == "video.extra_options" else None
 
         if fallback_keys:
-            logger.warning(
+            logging.warning(
                 f"Merging heterogeneous or incomplete video encoder metadata for feature {vk}. "
                 f"Setting these keys to null: {fallback_keys}.",
             )
@@ -117,7 +92,7 @@ def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMeta
     return merged_info
 
 
-def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]) -> tuple[int, str | None, FeatureDict]:
+def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]):
     """Validates that all dataset metadata have consistent properties.
 
     Ensures all datasets have the same fps, robot_type, and features to guarantee
@@ -154,9 +129,7 @@ def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]) -> tuple[i
     return fps, robot_type, features
 
 
-def update_data_df(
-    df: pd.DataFrame, src_meta: LeRobotDatasetMetadata, dst_meta: LeRobotDatasetMetadata
-) -> pd.DataFrame:
+def update_data_df(df, src_meta, dst_meta):
     """Updates a data DataFrame with new indices and task mappings for aggregation.
 
     Adjusts episode indices, frame indices, and task indices to account for
@@ -181,12 +154,12 @@ def update_data_df(
 
 
 def update_meta_data(
-    df: pd.DataFrame,
-    dst_meta: LeRobotDatasetMetadata,
-    meta_idx: IndexState,
-    data_idx: IndexState,
-    videos_idx: VideoIndexState,
-) -> pd.DataFrame:
+    df,
+    dst_meta,
+    meta_idx,
+    data_idx,
+    videos_idx,
+):
     """Updates metadata DataFrame with new chunk, file, and timestamp indices.
 
     Adjusts all indices and timestamps to account for previously aggregated
@@ -316,7 +289,7 @@ def aggregate_datasets(
     chunk_size: int | None = None,
     concatenate_videos: bool = True,
     concatenate_data: bool = True,
-) -> None:
+):
     """Aggregates multiple LeRobot datasets into a single unified dataset.
 
     This is the main function that orchestrates the aggregation process by:
@@ -336,7 +309,7 @@ def aggregate_datasets(
         concatenate_videos: When False, keep one mp4 per source file instead of packing into shards.
         concatenate_data: When False, keep one parquet per source file instead of packing into shards.
     """
-    logger.info("Start aggregate_datasets")
+    logging.info("Start aggregate_datasets")
 
     if data_files_size_in_mb is None:
         data_files_size_in_mb = DEFAULT_DATA_FILE_SIZE_IN_MB
@@ -368,15 +341,15 @@ def aggregate_datasets(
         video_files_size_in_mb=video_files_size_in_mb,
     )
 
-    logger.info("Find all tasks")
+    logging.info("Find all tasks")
     unique_tasks = pd.concat([m.tasks for m in all_metadata]).index.unique()
     dst_meta.tasks = pd.DataFrame(
         {"task_index": range(len(unique_tasks))}, index=pd.Index(unique_tasks, name="task")
     )
 
-    meta_idx: IndexState = {"chunk": 0, "file": 0}
-    data_idx: IndexState = {"chunk": 0, "file": 0}
-    videos_idx: VideoIndexState = {
+    meta_idx = {"chunk": 0, "file": 0}
+    data_idx = {"chunk": 0, "file": 0}
+    videos_idx = {
         key: {"chunk": 0, "file": 0, "latest_duration": 0, "episode_duration": 0} for key in video_keys
     }
 
@@ -400,17 +373,12 @@ def aggregate_datasets(
         dst_meta.info.total_frames += src_meta.total_frames
 
     finalize_aggregation(dst_meta, all_metadata)
-    logger.info("Aggregation complete.")
+    logging.info("Aggregation complete.")
 
 
 def aggregate_videos(
-    src_meta: LeRobotDatasetMetadata,
-    dst_meta: LeRobotDatasetMetadata,
-    videos_idx: VideoIndexState,
-    video_files_size_in_mb: float,
-    chunk_size: int,
-    concatenate_videos: bool = True,
-) -> VideoIndexState:
+    src_meta, dst_meta, videos_idx, video_files_size_in_mb, chunk_size, concatenate_videos=True
+):
     """Aggregates video chunks from a source dataset into the destination dataset.
 
     Handles video file concatenation and rotation based on file size limits.
@@ -438,16 +406,15 @@ def aggregate_videos(
             videos_idx[key]["dst_file_durations"] = {}
 
     for key, video_idx in videos_idx.items():
-        unique_chunk_file_pairs: list[ChunkFile] = sorted(
-            {
-                (chunk, file)
-                for chunk, file in zip(
-                    src_meta.episodes[f"videos/{key}/chunk_index"],
-                    src_meta.episodes[f"videos/{key}/file_index"],
-                    strict=False,
-                )
-            }
-        )
+        unique_chunk_file_pairs = {
+            (chunk, file)
+            for chunk, file in zip(
+                src_meta.episodes[f"videos/{key}/chunk_index"],
+                src_meta.episodes[f"videos/{key}/file_index"],
+                strict=False,
+            )
+        }
+        unique_chunk_file_pairs = sorted(unique_chunk_file_pairs)
 
         chunk_idx = video_idx["chunk"]
         file_idx = video_idx["file"]
@@ -522,14 +489,7 @@ def aggregate_videos(
     return videos_idx
 
 
-def aggregate_data(
-    src_meta: LeRobotDatasetMetadata,
-    dst_meta: LeRobotDatasetMetadata,
-    data_idx: IndexState,
-    data_files_size_in_mb: float,
-    chunk_size: int,
-    concatenate_data: bool = True,
-) -> IndexState:
+def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_size, concatenate_data=True):
     """Aggregates data chunks from a source dataset into the destination dataset.
 
     Reads source data files, updates indices to match the aggregated dataset,
@@ -550,16 +510,14 @@ def aggregate_data(
     Returns:
         dict: Updated data_idx with current chunk and file indices.
     """
-    unique_chunk_file_ids: list[ChunkFile] = sorted(
-        {
-            (c, f)
-            for c, f in zip(
-                src_meta.episodes["data/chunk_index"],
-                src_meta.episodes["data/file_index"],
-                strict=False,
-            )
-        }
-    )
+    unique_chunk_file_ids = {
+        (c, f)
+        for c, f in zip(
+            src_meta.episodes["data/chunk_index"], src_meta.episodes["data/file_index"], strict=False
+        )
+    }
+
+    unique_chunk_file_ids = sorted(unique_chunk_file_ids)
     contains_images = len(dst_meta.image_keys) > 0
 
     # retrieve features schema for proper image typing in parquet
@@ -567,7 +525,7 @@ def aggregate_data(
 
     # Track source to destination file mapping for metadata update
     # This is critical for handling datasets that are already results of a merge
-    src_to_dst: dict[ChunkFile, ChunkFile] = {}
+    src_to_dst: dict[tuple[int, int], tuple[int, int]] = {}
 
     for src_chunk_idx, src_file_idx in unique_chunk_file_ids:
         src_path = src_meta.root / DEFAULT_DATA_PATH.format(
@@ -606,13 +564,7 @@ def aggregate_data(
     return data_idx
 
 
-def aggregate_metadata(
-    src_meta: LeRobotDatasetMetadata,
-    dst_meta: LeRobotDatasetMetadata,
-    meta_idx: IndexState,
-    data_idx: IndexState,
-    videos_idx: VideoIndexState,
-) -> IndexState:
+def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
     """Aggregates metadata from a source dataset into the destination dataset.
 
     Reads source metadata files, updates all indices and timestamps,
@@ -628,16 +580,16 @@ def aggregate_metadata(
     Returns:
         dict: Updated meta_idx with current chunk and file indices.
     """
-    chunk_file_ids: list[ChunkFile] = sorted(
-        {
-            (c, f)
-            for c, f in zip(
-                src_meta.episodes["meta/episodes/chunk_index"],
-                src_meta.episodes["meta/episodes/file_index"],
-                strict=False,
-            )
-        }
-    )
+    chunk_file_ids = {
+        (c, f)
+        for c, f in zip(
+            src_meta.episodes["meta/episodes/chunk_index"],
+            src_meta.episodes["meta/episodes/file_index"],
+            strict=False,
+        )
+    }
+
+    chunk_file_ids = sorted(chunk_file_ids)
     for chunk_idx, file_idx in chunk_file_ids:
         src_path = src_meta.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
         df = pd.read_parquet(src_path)
@@ -670,16 +622,16 @@ def aggregate_metadata(
 def append_or_create_parquet_file(
     df: pd.DataFrame,
     src_path: Path,
-    idx: IndexState,
+    idx: dict[str, int],
     max_mb: float,
     chunk_size: int,
     default_path: str,
     contains_images: bool = False,
-    aggr_root: Path | None = None,
+    aggr_root: Path = None,
     hf_features: datasets.Features | None = None,
     concatenate: bool = True,
     one_row_group_per_episode: bool = False,
-) -> tuple[IndexState, ChunkFile]:
+) -> tuple[dict[str, int], tuple[int, int]]:
     """Appends data to an existing parquet file or creates a new one based on size constraints.
 
     Manages file rotation when size limits are exceeded to prevent individual files
@@ -702,13 +654,7 @@ def append_or_create_parquet_file(
     Returns:
         tuple: (updated_idx, (dst_chunk, dst_file)) where updated_idx is the index dict
                and (dst_chunk, dst_file) is the actual destination file the data was written to.
-
-    Raises:
-        ValueError: If aggr_root is not provided.
     """
-    if aggr_root is None:
-        raise ValueError("aggr_root must be provided.")
-
     dst_chunk, dst_file = idx["chunk"], idx["file"]
     dst_path = aggr_root / default_path.format(chunk_index=dst_chunk, file_index=dst_file)
 
@@ -752,9 +698,7 @@ def append_or_create_parquet_file(
     return idx, (dst_chunk, dst_file)
 
 
-def finalize_aggregation(
-    aggr_meta: LeRobotDatasetMetadata, all_metadata: list[LeRobotDatasetMetadata]
-) -> None:
+def finalize_aggregation(aggr_meta, all_metadata):
     """Finalizes the dataset aggregation by writing summary files and statistics.
 
     Writes the tasks file, info file with total counts and splits, and
@@ -764,16 +708,16 @@ def finalize_aggregation(
         aggr_meta: Aggregated dataset metadata.
         all_metadata: List of all source dataset metadata objects.
     """
-    logger.info("write tasks")
+    logging.info("write tasks")
     write_tasks(aggr_meta.tasks, aggr_meta.root)
 
-    logger.info("write info")
+    logging.info("write info")
     aggr_meta.info.total_tasks = len(aggr_meta.tasks)
     aggr_meta.info.total_episodes = sum(m.total_episodes for m in all_metadata)
     aggr_meta.info.total_frames = sum(m.total_frames for m in all_metadata)
     aggr_meta.info.splits = {"train": f"0:{sum(m.total_episodes for m in all_metadata)}"}
     write_info(aggr_meta.info, aggr_meta.root)
 
-    logger.info("write stats")
+    logging.info("write stats")
     aggr_meta.stats = aggregate_stats([m.stats for m in all_metadata])
     write_stats(aggr_meta.stats, aggr_meta.root)

src/lerobot/datasets/pipeline_features.py

@@ -17,8 +17,8 @@ from collections.abc import Sequence
 from typing import Any
 
 from lerobot.configs import PipelineFeatureType
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.processor import DataProcessorPipeline
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE, OBS_STR
 from lerobot.utils.feature_utils import hw_to_dataset_features
 

src/lerobot/datasets/streaming_dataset.py

@@ -58,10 +58,6 @@ class LookAheadError(Exception):
     pass
 
 
-class _ShardExhaustedError(Exception):
-    """Raised when a streaming dataset shard has no more items."""
-
-
 class Backtrackable[T]:
     """
     Wrap any iterator/iterable so you can step back up to `history` items
@@ -182,7 +178,7 @@ class Backtrackable[T]:
         """
         Check if we can go back `steps` items without raising an IndexError.
         """
-        return steps < len(self._back_buf) + self._cursor
+        return steps <= len(self._back_buf) + self._cursor
 
     def can_peek_ahead(self, steps: int = 1) -> bool:
         """
@@ -426,7 +422,10 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
                     else:
                         frames_buffer.append(frame)
                     break  # random shard sampled, switch shard
-            except _ShardExhaustedError:
+            except (
+                RuntimeError,
+                StopIteration,
+            ):  # NOTE: StopIteration inside a generator throws a RuntimeError since python 3.7
                 del idx_to_backtrack_dataset[shard_key]  # Remove exhausted shard, onto another shard
 
         # Once shards are all exhausted, shuffle the buffer and yield the remaining frames
@@ -504,11 +503,7 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
 
     def make_frame(self, dataset_iterator: Backtrackable) -> Generator:
         """Makes a frame starting from a dataset iterator"""
-        try:
-            item = next(dataset_iterator)
-        except StopIteration as e:
-            # Translate exhaustion here, before PEP 479 turns it into an indistinguishable RuntimeError.
-            raise _ShardExhaustedError from e
+        item = next(dataset_iterator)
         item = item_to_torch(item)
 
         updates = []  # list of "updates" to apply to the item retrieved from hf_dataset (w/o camera features)

src/lerobot/envs/configs.py

@@ -507,7 +507,7 @@ class MetaworldEnv(EnvConfig):
 class RoboCasaEnv(EnvConfig):
     task: str = "CloseFridge"
     fps: int = 20
-    episode_length: int | None = None
+    episode_length: int = 1000
     obs_type: str = "pixels_agent_pos"
     render_mode: str = "rgb_array"
     camera_name: str = "robot0_agentview_left,robot0_eye_in_hand,robot0_agentview_right"

src/lerobot/envs/libero.py

@@ -30,7 +30,7 @@ from gymnasium import spaces
 from libero.libero import benchmark, get_libero_path
 from libero.libero.envs import OffScreenRenderEnv
 
-from lerobot.lerobot_types import RobotObservation
+from lerobot.types import RobotObservation
 
 from .utils import _LazyAsyncVectorEnv, parse_camera_names
 
@@ -384,12 +384,7 @@ class LiberoEnv(gym.Env):
 
     def close(self):
         if self._env is not None:
-            try:
-                self._env.close()
-            finally:
-                # LIBERO deletes its inner env on close, so this wrapper must
-                # be recreated before the next reset.
-                self._env = None
+            self._env.close()
 
 
 def _make_env_fns(

src/lerobot/envs/metaworld.py

@@ -25,7 +25,7 @@ import metaworld.policies as policies
 import numpy as np
 from gymnasium import spaces
 
-from lerobot.lerobot_types import RobotObservation
+from lerobot.types import RobotObservation
 
 from .utils import _LazyAsyncVectorEnv
 

src/lerobot/envs/robocasa.py

@@ -25,7 +25,7 @@ import gymnasium as gym
 import numpy as np
 from gymnasium import spaces
 
-from lerobot.lerobot_types import RobotObservation
+from lerobot.types import RobotObservation
 
 from .utils import _LazyAsyncVectorEnv, parse_camera_names
 
@@ -98,19 +98,6 @@ def _resolve_tasks(task: str) -> tuple[list[str], str | None]:
     return names, None
 
 
-def _get_task_horizon(task: str) -> int:
-    """Return the rollout horizon registered by RoboCasa for a task."""
-    from robocasa.utils.dataset_registry_utils import get_task_horizon
-
-    try:
-        return int(get_task_horizon(task))
-    except ValueError as exc:
-        raise ValueError(
-            f"No RoboCasa horizon is registered for task '{task}'. "
-            "Set `--env.episode_length=<steps>` explicitly."
-        ) from exc
-
-
 def convert_action(flat_action: np.ndarray) -> dict[str, Any]:
     """Split a flat (12,) action vector into a RoboCasa action dict.
 
@@ -167,7 +154,7 @@ class RoboCasaEnv(gym.Env):
 
         self.camera_name = parse_camera_names(camera_name)
 
-        self._max_episode_steps = episode_length if episode_length is not None else _get_task_horizon(task)
+        self._max_episode_steps = episode_length if episode_length is not None else 1000
 
         # Deferred — created on first reset() inside the worker subprocess
         # to avoid inheriting stale GPU/EGL contexts across fork().

src/lerobot/envs/robotwin.py

@@ -28,7 +28,7 @@ import numpy as np
 import torch
 from gymnasium import spaces
 
-from lerobot.lerobot_types import RobotObservation
+from lerobot.types import RobotObservation
 from lerobot.utils.import_utils import _scipy_available
 
 from .utils import _LazyAsyncVectorEnv

src/lerobot/envs/vlabench.py

@@ -37,7 +37,7 @@ import numpy as np
 from gymnasium import spaces
 from scipy.spatial.transform import Rotation
 
-from lerobot.lerobot_types import RobotObservation
+from lerobot.types import RobotObservation
 
 from .utils import _LazyAsyncVectorEnv
 

src/lerobot/policies/eo1/processor_eo1.py

@@ -22,7 +22,6 @@ from typing import TYPE_CHECKING, Any
 import torch
 
 from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import TransitionKey
 from lerobot.processor import (
     ComplementaryDataProcessorStep,
     PolicyAction,
@@ -32,6 +31,7 @@ from lerobot.processor import (
     make_default_policy_processor_steps,
     make_policy_processor_pipelines,
 )
+from lerobot.types import TransitionKey
 from lerobot.utils.constants import OBS_STATE
 from lerobot.utils.import_utils import _transformers_available, require_package
 

src/lerobot/policies/evo1/modeling_evo1.py

@@ -42,9 +42,6 @@ class Evo1Policy(PreTrainedPolicy):
     config_class = Evo1Config
     name = "evo1"
 
-    def supports_rtc(self) -> bool:
-        return True
-
     def __init__(self, config: Evo1Config, *, vlm_hub_kwargs: dict | None = None, **kwargs):
         super().__init__(config)
         config.validate_features()

src/lerobot/policies/evo1/processor_evo1.py

@@ -21,7 +21,6 @@ from typing import Any
 import torch
 
 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DeviceProcessorStep,
@@ -41,6 +40,7 @@ from lerobot.processor.converters import (
     policy_action_to_transition,
     transition_to_policy_action,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
     ACTION,
     DONE,
@@ -302,33 +302,6 @@ def _pad_evo1_stats(
     return padded_stats
 
 
-def _refresh_evo1_normalization_steps(
-    config: Evo1Config,
-    preprocessor: PolicyProcessorPipeline,
-    postprocessor: PolicyProcessorPipeline,
-) -> None:
-    """Re-pad checkpoint-loaded (un)normalizer stats/features to EVO1's fixed widths.
-
-    Loading a checkpoint injects the raw dataset stats (unpadded to max_state_dim/max_action_dim)
-    into the (un)normalizer via the generic override path in make_pre_post_processors. Those stats
-    and their declared features must be re-padded/reshaped to EVO1's fixed widths, otherwise
-    normalization fails against the padded state/action tensors (e.g. state padded to 24 vs. 8-dim
-    LIBERO stats). Padding is a no-op when stats are already at the target width.
-    """
-    normalization_features = _evo1_normalization_features(config)
-    action_features = _evo1_action_features(config)
-    for step in preprocessor.steps:
-        if isinstance(step, NormalizerProcessorStep):
-            step.features = normalization_features
-            step.stats = _pad_evo1_stats(config, step.stats)
-            step.to(device=step.device, dtype=step.dtype)
-    for step in postprocessor.steps:
-        if isinstance(step, UnnormalizerProcessorStep):
-            step.features = action_features
-            step.stats = _pad_evo1_stats(config, step.stats)
-            step.to(device=step.device, dtype=step.dtype)
-
-
 def reconcile_evo1_processors(
     config: Evo1Config,
     preprocessor: PolicyProcessorPipeline,
@@ -336,19 +309,16 @@ def reconcile_evo1_processors(
 ) -> tuple[PolicyProcessorPipeline, PolicyProcessorPipeline]:
     """Reconcile checkpoint-loaded pipelines with the current EVO1 config.
 
-    Three things cannot be restored from a serialized pipeline alone: the EVO1 batch converter
-    (converters are plain functions and are never serialized), eval-time CLI overrides of the
-    action postprocessing flags (`postprocess_action_dim`, `binarize_gripper`, `gripper_*`), and the
-    (un)normalizer stats/features when the generic override path injects raw, unpadded dataset
-    stats. This restores the converter, re-pads the normalization stats to EVO1's fixed widths, and
-    rebuilds the action step from the current config so those overrides take effect.
+    Two things cannot be restored from a serialized pipeline alone: the EVO1 batch converter
+    (converters are plain functions and are never serialized), and eval-time CLI overrides of the
+    action postprocessing flags (`postprocess_action_dim`, `binarize_gripper`, `gripper_*`). This
+    restores the converter and rebuilds the action step from the current config so those overrides
+    take effect.
     """
     # Pipelines reloaded from a checkpoint come back with the default batch converter, which drops
     # non-observation extras (embodiment_id, state_mask, custom task fields) needed by EVO1.
     preprocessor.to_transition = evo1_batch_to_transition
 
-    _refresh_evo1_normalization_steps(config, preprocessor, postprocessor)
-
     action_step = Evo1ActionProcessorStep(
         action_dim=_evo1_action_dim(config),
         binarize_gripper=config.binarize_gripper,

src/lerobot/policies/factory.py

@@ -28,7 +28,6 @@ if TYPE_CHECKING:
 
 from lerobot.configs import FeatureType, PreTrainedConfig
 from lerobot.envs import EnvConfig, env_to_policy_features
-from lerobot.lerobot_types import PolicyAction
 from lerobot.processor import (
     AbsoluteActionsProcessorStep,
     PolicyProcessorPipeline,
@@ -38,6 +37,7 @@ from lerobot.processor import (
     transition_to_batch,
     transition_to_policy_action,
 )
+from lerobot.types import PolicyAction
 from lerobot.utils.constants import (
     ACTION,
     POLICY_POSTPROCESSOR_DEFAULT_NAME,

src/lerobot/policies/groot/modeling_groot.py

@@ -68,9 +68,6 @@ class GrootPolicy(PreTrainedPolicy):
     name = "groot"
     config_class = GrootConfig
 
-    def supports_rtc(self) -> bool:
-        return True
-
     def __init__(self, config: GrootConfig, **kwargs):
         """Initialize Groot policy wrapper."""
         require_package("transformers", extra="groot")

src/lerobot/policies/groot/processor_groot.py

@@ -50,7 +50,6 @@ if TYPE_CHECKING or _datasets_available:
 else:
     LeRobotDataset = None
 
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
     AbsoluteActionsProcessorStep,
     AddBatchDimensionProcessorStep,
@@ -67,6 +66,7 @@ from lerobot.processor import (
     transition_to_batch,
     transition_to_policy_action,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
     ACTION,
     OBS_IMAGE,

src/lerobot/policies/molmoact2/modeling_molmoact2.py

@@ -520,9 +520,6 @@ class MolmoAct2Policy(PreTrainedPolicy):
     config_class = MolmoAct2Config
     name = "molmoact2"
 
-    def supports_rtc(self) -> bool:
-        return self.config.inference_action_mode == "continuous"
-
     def __init__(
         self,
         config: MolmoAct2Config,

src/lerobot/policies/molmoact2/processor_molmoact2.py

@@ -36,7 +36,6 @@ import torch
 from torch import Tensor
 
 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DeviceProcessorStep,
@@ -50,6 +49,7 @@ from lerobot.processor import (
     policy_action_to_transition,
     transition_to_policy_action,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
     ACTION,
     OBS_IMAGES,

src/lerobot/policies/pi0/modeling_pi0.py

@@ -749,9 +749,6 @@ class PI0Policy(PreTrainedPolicy):
     config_class = PI0Config
     name = "pi0"
 
-    def supports_rtc(self) -> bool:
-        return True
-
     def __init__(
         self,
         config: PI0Config,

src/lerobot/policies/pi05/modeling_pi05.py

@@ -714,9 +714,6 @@ class PI05Policy(PreTrainedPolicy):
     config_class = PI05Config
     name = "pi05"
 
-    def supports_rtc(self) -> bool:
-        return True
-
     def __init__(
         self,
         config: PI05Config,

src/lerobot/policies/pi05/processor_pi05.py

@@ -22,7 +22,6 @@ import numpy as np
 import torch
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
     AbsoluteActionsProcessorStep,
     PolicyAction,
@@ -34,6 +33,7 @@ from lerobot.processor import (
     make_default_policy_processor_steps,
     make_policy_processor_pipelines,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import OBS_STATE
 
 from .configuration_pi05 import PI05Config

src/lerobot/policies/pi0_fast/processor_pi0_fast.py

@@ -22,7 +22,6 @@ import numpy as np
 import torch
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
     AbsoluteActionsProcessorStep,
     ActionTokenizerProcessorStep,
@@ -35,6 +34,7 @@ from lerobot.processor import (
     make_default_policy_processor_steps,
     make_policy_processor_pipelines,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import OBS_STATE
 
 from .configuration_pi0_fast import PI0FastConfig

src/lerobot/policies/pretrained.py

@@ -249,10 +249,6 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         """
         raise NotImplementedError
 
-    def supports_rtc(self) -> bool:
-        """Whether this policy implements Real-Time Chunking inference semantics."""
-        return False
-
     # TODO(aliberts, rcadene): split into 'forward' and 'compute_loss'?
     @abc.abstractmethod
     def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict | None]:

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -145,9 +145,6 @@ class SmolVLAPolicy(PreTrainedPolicy):
     config_class = SmolVLAConfig
     name = "smolvla"
 
-    def supports_rtc(self) -> bool:
-        return True
-
     def __init__(
         self,
         config: SmolVLAConfig,

src/lerobot/policies/smolvla/smolvlm_with_expert.py

@@ -168,23 +168,14 @@ class SmolVLMWithExpertModel(nn.Module):
                 last_layers.append(self.num_vlm_layers - 2)
             frozen_layers = [
                 "lm_head",
-                "text_model.norm.weight",
+                "text_model.model.norm.weight",
             ]
             for layer in last_layers:
-                frozen_layers.append(f"text_model.layers.{layer}.")
+                frozen_layers.append(f"text_model.model.layers.{layer}.")
 
-            unmatched_patterns = set(frozen_layers)
             for name, params in self.vlm.named_parameters():
-                matched_patterns = [k for k in frozen_layers if k in name]
-                if matched_patterns:
+                if any(k in name for k in frozen_layers):
                     params.requires_grad = False
-                    unmatched_patterns.difference_update(matched_patterns)
-            if unmatched_patterns:
-                raise RuntimeError(
-                    "Some frozen layer patterns matched no VLM parameters, so the corresponding layers "
-                    "would silently remain trainable (parameter naming may have changed in transformers): "
-                    f"{sorted(unmatched_patterns)}"
-                )
         # To avoid unused params issue with distributed training
         for name, params in self.lm_expert.named_parameters():
             if "lm_head" in name:

src/lerobot/policies/utils.py

@@ -22,7 +22,7 @@ import torch
 from torch import nn
 
 from lerobot.configs import FeatureType, PolicyFeature, PreTrainedConfig
-from lerobot.lerobot_types import PolicyAction, RobotAction, RobotObservation
+from lerobot.types import PolicyAction, RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame
 

src/lerobot/policies/vla_jepa/configuration_vla_jepa.py

@@ -15,13 +15,11 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from typing import Any
 
 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.configs.types import NormalizationMode
 from lerobot.optim.optimizers import AdamWConfig
 from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
-from lerobot.utils.constants import OBS_STATE
 
 
 @PreTrainedConfig.register_subclass("vla_jepa")
@@ -124,13 +122,6 @@ class VLAJEPAConfig(PreTrainedConfig):
         if self.robot_state_feature is not None:
             self.state_dim = self.robot_state_feature.shape[0]
 
-    def set_dataset_feature_metadata(self, dataset_features: dict[str, Any]) -> None:
-        """Add `observation.state` to `input_features` if missing, so it gets normalized."""
-        if OBS_STATE in self.input_features or OBS_STATE not in dataset_features:
-            return
-        shape = tuple(dataset_features[OBS_STATE]["shape"])
-        self.input_features[OBS_STATE] = PolicyFeature(type=FeatureType.STATE, shape=shape)
-
     def get_optimizer_preset(self) -> AdamWConfig:
         return AdamWConfig(
             lr=self.optimizer_lr,

src/lerobot/policies/vla_jepa/modeling_vla_jepa.py

@@ -399,8 +399,7 @@ class VLAJEPAPolicy(PreTrainedPolicy):
         state = batch.get(OBS_STATE)
         if state is not None:
             if state.ndim > 2:
-                # deltas are forward-looking here, so index 0 is the current observation, not -1.
-                state = state[:, 0, :]
+                state = state[:, -1, :]
             inputs["state"] = (state.unsqueeze(1) if state.ndim == 2 else state).float()  # [B, 1, dim]
 
         return inputs

src/lerobot/policies/xvla/modeling_xvla.py

@@ -150,7 +150,7 @@ class XVLAModel(nn.Module):
         # Freeze or unfreeze policy transformer
         if not self.config.train_policy_transformer:
             for name, param in self.transformer.named_parameters():
-                if "soft_prompt" not in name:
+                if "soft_prompts" not in name:
                     param.requires_grad = False
 
         # Freeze or unfreeze soft prompts

src/lerobot/policies/xvla/processor_xvla.py

@@ -21,7 +21,6 @@ import numpy as np
 import torch
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
     ObservationProcessorStep,
     PolicyAction,
@@ -32,6 +31,7 @@ from lerobot.processor import (
     make_default_policy_processor_steps,
     make_policy_processor_pipelines,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
     IMAGENET_STATS,
     OBS_IMAGES,

src/lerobot/processor/__init__.py

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from lerobot.lerobot_types import (
+from lerobot.types import (
     EnvAction,
     EnvTransition,
     PolicyAction,

src/lerobot/processor/batch_processor.py

@@ -25,7 +25,7 @@ from dataclasses import dataclass, field
 from torch import Tensor
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, PolicyAction
+from lerobot.types import EnvTransition, PolicyAction
 from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
 
 from .pipeline import (

src/lerobot/processor/converters.py

@@ -23,7 +23,7 @@ from typing import Any
 import numpy as np
 import torch
 
-from lerobot.lerobot_types import EnvTransition, PolicyAction, RobotAction, RobotObservation, TransitionKey
+from lerobot.types import EnvTransition, PolicyAction, RobotAction, RobotObservation, TransitionKey
 from lerobot.utils.constants import ACTION, DONE, INFO, OBS_PREFIX, REWARD, TRUNCATED
 
 

src/lerobot/processor/delta_action_processor.py

@@ -17,7 +17,7 @@
 from dataclasses import dataclass
 
 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import PolicyAction, RobotAction
+from lerobot.types import PolicyAction, RobotAction
 
 from .pipeline import ActionProcessorStep, ProcessorStepRegistry, RobotActionProcessorStep
 

src/lerobot/processor/device_processor.py

@@ -25,7 +25,7 @@ from typing import Any
 import torch
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, PolicyAction, TransitionKey
+from lerobot.types import EnvTransition, PolicyAction, TransitionKey
 from lerobot.utils.device_utils import get_safe_torch_device
 
 from .pipeline import ProcessorStep, ProcessorStepRegistry

src/lerobot/processor/factory.py

@@ -20,7 +20,7 @@ from typing import Any
 import torch
 
 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.lerobot_types import PolicyAction, RobotAction, RobotObservation
+from lerobot.types import PolicyAction, RobotAction, RobotObservation
 from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
 
 from .batch_processor import AddBatchDimensionProcessorStep

src/lerobot/processor/gym_action_processor.py

@@ -17,7 +17,7 @@
 from dataclasses import dataclass
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvAction, EnvTransition, PolicyAction, TransitionKey
+from lerobot.types import EnvAction, EnvTransition, PolicyAction, TransitionKey
 
 from .converters import to_tensor
 from .hil_processor import TELEOP_ACTION_KEY

src/lerobot/processor/hil_processor.py

@@ -29,7 +29,7 @@ from lerobot.teleoperators.utils import TeleopEvents
 if TYPE_CHECKING:
     from lerobot.teleoperators.teleoperator import Teleoperator
 
-from lerobot.lerobot_types import EnvTransition, PolicyAction, TransitionKey
+from lerobot.types import EnvTransition, PolicyAction, TransitionKey
 
 from .pipeline import (
     ComplementaryDataProcessorStep,

src/lerobot/processor/normalize_processor.py

@@ -25,7 +25,7 @@ import torch
 from torch import Tensor
 
 from lerobot.configs import FeatureType, NormalizationMode, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, PolicyAction, TransitionKey
+from lerobot.types import EnvTransition, PolicyAction, TransitionKey
 
 if TYPE_CHECKING:
     from lerobot.datasets import LeRobotDataset

src/lerobot/processor/pipeline.py

@@ -45,14 +45,7 @@ from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file, save_file
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import (
-    EnvAction,
-    EnvTransition,
-    PolicyAction,
-    RobotAction,
-    RobotObservation,
-    TransitionKey,
-)
+from lerobot.types import EnvAction, EnvTransition, PolicyAction, RobotAction, RobotObservation, TransitionKey
 from lerobot.utils.constants import HF_LEROBOT_HOME
 from lerobot.utils.hub import HubMixin
 

src/lerobot/processor/policy_robot_bridge.py

@@ -20,7 +20,7 @@ from typing import Any
 import torch
 
 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import PolicyAction, RobotAction
+from lerobot.types import PolicyAction, RobotAction
 from lerobot.utils.constants import ACTION
 
 from .pipeline import ActionProcessorStep, ProcessorStepRegistry

src/lerobot/processor/relative_action_processor.py

@@ -20,7 +20,7 @@ import torch
 from torch import Tensor
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import OBS_STATE
 
 from .delta_action_processor import MapDeltaActionToRobotActionStep, MapTensorToDeltaActionDictStep

src/lerobot/processor/render_messages_processor.py

@@ -23,7 +23,7 @@ from lerobot.configs import PipelineFeatureType, PolicyFeature
 from lerobot.configs.recipe import TrainingRecipe
 from lerobot.datasets.language import LANGUAGE_EVENTS, LANGUAGE_PERSISTENT
 from lerobot.datasets.language_render import render_sample
-from lerobot.lerobot_types import EnvTransition, TransitionKey
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.utils import unwrap_scalar
 
 from .pipeline import ProcessorStep, ProcessorStepRegistry

src/lerobot/processor/tokenizer_processor.py

@@ -30,7 +30,7 @@ from typing import TYPE_CHECKING, Any
 import torch
 
 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, RobotObservation, TransitionKey
+from lerobot.types import EnvTransition, RobotObservation, TransitionKey
 from lerobot.utils.constants import (
     ACTION_TOKEN_MASK,
     ACTION_TOKENS,

src/lerobot/rewards/robometer/compute_rabc_weights.py

@@ -57,10 +57,10 @@ import torch
 from tqdm import tqdm
 
 from lerobot.datasets import LeRobotDataset
-from lerobot.lerobot_types import TransitionKey
 from lerobot.rewards.robometer.configuration_robometer import RobometerConfig
 from lerobot.rewards.robometer.modeling_robometer import RobometerRewardModel
 from lerobot.rewards.robometer.processor_robometer import RobometerEncoderProcessorStep
+from lerobot.types import TransitionKey
 
 DEFAULT_OUTPUT_FILENAME = "robometer_progress.parquet"
 

src/lerobot/rewards/robometer/processor_robometer.py

@@ -25,7 +25,6 @@ from PIL import Image
 from torch import Tensor
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DeviceProcessorStep,
@@ -40,6 +39,7 @@ from lerobot.rewards.robometer.configuration_robometer import (
     RobometerConfig,
 )
 from lerobot.rewards.robometer.modeling_robometer import ROBOMETER_FEATURE_PREFIX
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
     OBS_IMAGES,
     POLICY_POSTPROCESSOR_DEFAULT_NAME,

src/lerobot/rewards/sarm/processor_sarm.py

@@ -47,7 +47,6 @@ else:
     Faker = None  # type: ignore[assignment, misc]
 
 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, PolicyAction, TransitionKey
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DeviceProcessorStep,
@@ -59,6 +58,7 @@ from lerobot.processor import (
     policy_action_to_transition,
     transition_to_policy_action,
 )
+from lerobot.types import EnvTransition, PolicyAction, TransitionKey
 from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
 
 from .configuration_sarm import SARMConfig

src/lerobot/rewards/topreward/compute_rabc_weights.py

@@ -48,10 +48,10 @@ import torch
 from tqdm import tqdm
 
 from lerobot.datasets import LeRobotDataset
-from lerobot.lerobot_types import TransitionKey
 from lerobot.rewards.topreward.configuration_topreward import TOPRewardConfig
 from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
 from lerobot.rewards.topreward.processor_topreward import TOPRewardEncoderProcessorStep
+from lerobot.types import TransitionKey
 
 DEFAULT_OUTPUT_FILENAME = "topreward_progress.parquet"
 

src/lerobot/rewards/topreward/processor_topreward.py

@@ -23,7 +23,6 @@ import torch
 from torch import Tensor
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DeviceProcessorStep,
@@ -38,6 +37,7 @@ from lerobot.rewards.topreward.configuration_topreward import (
     DEFAULT_PROMPT_SUFFIX_TEMPLATE,
     TOPRewardConfig,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
     OBS_IMAGES,
     OBS_PREFIX,

src/lerobot/rl/algorithms/base.py

@@ -28,7 +28,7 @@ from huggingface_hub.errors import HfHubHTTPError
 from safetensors.torch import load_file as load_safetensors, save_file as save_safetensors
 from torch.optim import Optimizer
 
-from lerobot.lerobot_types import BatchType
+from lerobot.types import BatchType
 from lerobot.utils.hub import HubMixin
 
 from .configs import RLAlgorithmConfig, TrainingStats

src/lerobot/rl/algorithms/configs.py

@@ -16,7 +16,6 @@ from __future__ import annotations
 
 import abc
 import builtins
-import json
 import logging
 import os
 from dataclasses import dataclass, field
@@ -79,10 +78,8 @@ class RLAlgorithmConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
 
     def _save_pretrained(self, save_directory: Path) -> None:
         """Serialize this config as ``config.json`` inside ``save_directory``."""
-        # Encode against the base class so draccus includes the choice "type" key,
-        # which `from_pretrained` needs to resolve the concrete subclass.
-        with open(save_directory / CONFIG_NAME, "w") as f:
-            json.dump(draccus.encode(self, RLAlgorithmConfig), f, indent=4)
+        with open(save_directory / CONFIG_NAME, "w") as f, draccus.config_type("json"):
+            draccus.dump(self, f, indent=4)
 
     @classmethod
     def from_pretrained(

src/lerobot/rl/algorithms/sac/sac_algorithm.py

@@ -26,7 +26,6 @@ import torch.nn.functional as F  # noqa: N812
 from torch import Tensor
 from torch.optim import Optimizer
 
-from lerobot.lerobot_types import BatchType
 from lerobot.policies.gaussian_actor.modeling_gaussian_actor import (
     DISCRETE_DIMENSION_INDEX,
     MLP,
@@ -36,6 +35,7 @@ from lerobot.policies.gaussian_actor.modeling_gaussian_actor import (
     orthogonal_init,
 )
 from lerobot.policies.utils import get_device_from_parameters
+from lerobot.types import BatchType
 from lerobot.utils.constants import ACTION
 from lerobot.utils.transition import move_state_dict_to_device
 

src/lerobot/rl/buffer.py

@@ -18,7 +18,7 @@ import functools
 import threading
 from collections.abc import Callable, Sequence
 from contextlib import suppress
-from typing import NotRequired, TypedDict
+from typing import TypedDict
 
 import torch
 import torch.nn.functional as F  # noqa: N812
@@ -36,7 +36,7 @@ class BatchTransition(TypedDict):
     next_state: dict[str, torch.Tensor]
     done: torch.Tensor
     truncated: torch.Tensor
-    complementary_info: NotRequired[dict[str, torch.Tensor | float | int] | None]
+    complementary_info: dict[str, torch.Tensor | float | int] | None = None
 
 
 def random_crop_vectorized(images: torch.Tensor, output_size: tuple) -> torch.Tensor:

src/lerobot/rl/data_sources/__init__.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from lerobot.lerobot_types import BatchType
+from lerobot.types import BatchType
 
 from .data_mixer import DataMixer, OnlineOfflineMixer
 

src/lerobot/rl/data_sources/data_mixer.py

@@ -16,7 +16,7 @@ from __future__ import annotations
 
 import abc
 
-from lerobot.lerobot_types import BatchType
+from lerobot.types import BatchType
 
 from ..buffer import ReplayBuffer, concatenate_batch_transitions
 

src/lerobot/rl/trainer.py

@@ -17,7 +17,7 @@ from __future__ import annotations
 from collections.abc import Iterator
 from typing import Any
 
-from lerobot.lerobot_types import BatchType
+from lerobot.types import BatchType
 
 from .algorithms.base import RLAlgorithm
 from .algorithms.configs import TrainingStats

src/lerobot/robots/bi_openarm_follower/bi_openarm_follower.py

@@ -17,7 +17,7 @@
 import logging
 from functools import cached_property
 
-from lerobot.lerobot_types import RobotAction, RobotObservation
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.bimanual import BimanualMixin
 from lerobot.utils.decorators import check_if_not_connected
 

src/lerobot/robots/bi_rebot_b601_follower/bi_rebot_b601_follower.py

@@ -17,7 +17,7 @@
 import logging
 from functools import cached_property
 
-from lerobot.lerobot_types import RobotAction, RobotObservation
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.bimanual import BimanualMixin
 from lerobot.utils.decorators import check_if_not_connected
 

src/lerobot/robots/bi_so_follower/bi_so_follower.py

@@ -17,7 +17,7 @@
 import logging
 from functools import cached_property
 
-from lerobot.lerobot_types import RobotAction, RobotObservation
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.bimanual import BimanualMixin
 from lerobot.utils.decorators import check_if_not_connected
 
@@ -62,7 +62,6 @@ class BiSOFollower(BimanualMixin, Robot):
             position_i_coefficient=config.left_arm_config.position_i_coefficient,
             position_d_coefficient=config.left_arm_config.position_d_coefficient,
             use_degrees=config.left_arm_config.use_degrees,
-            num_read_retries=config.left_arm_config.num_read_retries,
             cameras=left_arm_cameras,
         )
 
@@ -76,7 +75,6 @@ class BiSOFollower(BimanualMixin, Robot):
             position_i_coefficient=config.right_arm_config.position_i_coefficient,
             position_d_coefficient=config.right_arm_config.position_d_coefficient,
             use_degrees=config.right_arm_config.use_degrees,
-            num_read_retries=config.right_arm_config.num_read_retries,
             cameras=config.right_arm_config.cameras,
         )
 

src/lerobot/robots/earthrover_mini_plus/robot_earthrover_mini_plus.py

@@ -23,7 +23,7 @@ import cv2
 import numpy as np
 import requests
 
-from lerobot.lerobot_types import RobotAction, RobotObservation
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 from lerobot.utils.errors import DeviceNotConnectedError
 

src/lerobot/robots/hope_jr/hope_jr_arm.py

@@ -19,12 +19,12 @@ import time
 from functools import cached_property
 
 from lerobot.cameras import make_cameras_from_configs
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.motors import Motor, MotorNormMode
 from lerobot.motors.calibration_gui import RangeFinderGUI
 from lerobot.motors.feetech import (
     FeetechMotorsBus,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..robot import Robot

src/lerobot/robots/hope_jr/hope_jr_hand.py

@@ -19,12 +19,12 @@ import time
 from functools import cached_property
 
 from lerobot.cameras import make_cameras_from_configs
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.motors import Motor, MotorNormMode
 from lerobot.motors.calibration_gui import RangeFinderGUI
 from lerobot.motors.feetech import (
     FeetechMotorsBus,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..robot import Robot

src/lerobot/robots/koch_follower/koch_follower.py

@@ -19,12 +19,12 @@ import time
 from functools import cached_property
 
 from lerobot.cameras import make_cameras_from_configs
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
 from lerobot.motors.dynamixel import (
     DynamixelMotorsBus,
     OperatingMode,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..robot import Robot

src/lerobot/robots/lekiwi/lekiwi.py

@@ -23,12 +23,12 @@ from typing import Any
 import numpy as np
 
 from lerobot.cameras import make_cameras_from_configs
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
 from lerobot.motors.feetech import (
     FeetechMotorsBus,
     OperatingMode,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..robot import Robot