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5 Commits
| Author | SHA1 | Date | |
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| fef1f0ca98 | |||
| ac5c7b8600 | |||
| a6befef0ba | |||
| 53843007ea | |||
| d3bed0feee |
@@ -51,6 +51,7 @@ pre-commit run --all-files # Lint + format (ruff, typo
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## Notes
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- **Mypy is gradual**: strict only for `lerobot.envs`, `lerobot.configs`, `lerobot.optim`, `lerobot.model`, `lerobot.cameras`, `lerobot.motors`, `lerobot.transport`. Add type annotations when modifying these modules.
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- **Optional dependencies**: many policies, envs, and robots are behind extras (e.g., `lerobot[aloha]`). New imports for optional packages must be guarded or lazy. See `pyproject.toml [project.optional-dependencies]`.
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- **Imports**: prefer top-level imports; relative (`from .sibling import X`) across sibling files within a module, absolute (`from lerobot.module import X`) across modules.
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- **Optional dependencies**: many policies, envs, and robots are behind extras (e.g., `lerobot[aloha]`, see `pyproject.toml`). Guard optional imports with `TYPE_CHECKING or _foo_available` at module top + a `require_package(...)` check at use time. Reuse the `_foo_available` flags in `utils/import_utils.py`; don't call `is_package_available`.
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- **Video decoding**: datasets can store observations as video files. `LeRobotDataset` handles frame extraction, but tests need ffmpeg installed.
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- **Prioritize use of `uv run`** to execute Python commands (not raw `python` or `pip`).
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@@ -165,6 +165,8 @@ Batches are flat dictionaries keyed by the constants in [`lerobot.utils.constant
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LeRobot uses `PolicyProcessorPipeline`s to normalize inputs and de-normalize outputs around your policy. For a concrete reference, see [`processor_act.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/act/processor_act.py) or [`processor_diffusion.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/diffusion/processor_diffusion.py).
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Pay close attention here: processors are the most common reproducibility pain point. A mismatch in normalization mode (`IDENTITY` vs `MEAN_STD` vs `MIN_MAX` vs `QUANTILES`/`QUANTILE10`) or in which features get normalized will train and eval without erroring, yet silently wreck results. Make sure the modes match how the checkpoint was trained, that the required stats exist (e.g. `QUANTILES` needs `q01`/`q99`), and that the pre- and post-processors stay consistent.
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```python
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# processor_my_policy.py
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from typing import Any
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@@ -304,7 +306,9 @@ Mirror an existing policy that's structurally similar to yours; the diff is smal
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### Heavy / optional dependencies
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Most policies need a heavy backbone (transformers, diffusers, a specific VLM SDK). The convention is **two-step gating**: a `TYPE_CHECKING`-guarded import at module top, and a `require_package` runtime check in the constructor. [`modeling_diffusion.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/diffusion/modeling_diffusion.py) is the canonical reference:
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Most policies need a heavy backbone (transformers, diffusers, a specific VLM SDK). Wherever one exists, prefer loading it e.g from `transformers` or `diffusers` rather than re-implementing the architecture in-tree.
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The convention is **two-step gating**: a `TYPE_CHECKING`-guarded import at module top, and a `require_package` runtime check in the constructor. [`modeling_diffusion.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/diffusion/modeling_diffusion.py) is the canonical reference:
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```python
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from typing import TYPE_CHECKING
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@@ -374,6 +378,7 @@ The general expectations are in [`CONTRIBUTING.md`](https://github.com/huggingfa
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- [ ] Optional deps live behind a `[project.optional-dependencies]` extra and the `TYPE_CHECKING + require_package` guard.
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- [ ] `tests/policies/` updated; backward-compat artifact committed & policy-specific tests.
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- [ ] `src/lerobot/policies/<name>/README.md` symlinked into `docs/source/policy_<name>_README.md`; user-facing `docs/source/<name>.mdx` written and added to `_toctree.yml`.
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- [ ] `lerobot-train --policy.type my_policy ...` runs end-to-end for at least a few steps + save a checkpoint that can be loaded and run by `lerobot-eval` or `lerobot-rollout`.
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- [ ] `templates/lerobot_modelcard_template.md` has a description entry and a `policy_docs` link for your policy.
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- [ ] The models table in the root `README.md` lists your policy in the right category, linking to your doc page.
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- [ ] At least one reproducible benchmark eval in the policy MDX with a published checkpoint (sim benchmark, or real-robot dataset + checkpoint).
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+2
-2
@@ -155,7 +155,7 @@ accelerate-dep = ["accelerate>=1.14.0,<2.0.0"]
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can-dep = ["python-can>=4.2.0,<5.0.0"]
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peft-dep = ["peft>=0.18.0,<1.0.0"]
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scipy-dep = ["scipy>=1.14.0,<2.0.0"]
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diffusers-dep = ["diffusers>=0.27.2,<0.36.0"]
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diffusers-dep = ["diffusers>=0.38.0,<0.40.0"]
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qwen-vl-utils-dep = ["qwen-vl-utils>=0.0.11,<0.1.0"]
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matplotlib-dep = ["matplotlib>=3.10.3,<4.0.0", "contourpy>=1.3.0,<2.0.0"] # NOTE: Explicitly listing contourpy helps the resolver converge faster.
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pyserial-dep = ["pyserial>=3.5,<4.0"]
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@@ -261,7 +261,7 @@ annotations = [
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# Development
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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]"]
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notebook = ["jupyter>=1.0.0,<2.0.0", "ipykernel>=6.0.0,<7.0.0"]
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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'"]
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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'"]
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video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
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# Simulation
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@@ -37,19 +37,13 @@ def is_image_feature(key: str) -> bool:
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@dataclass
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class ConcurrencyConfig:
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"""Configuration for the concurrency of the actor and learner.
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Possible values are:
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- "threads": Use threads for the actor and learner.
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- "processes": Use processes for the actor and learner.
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``multiprocessing_context`` selects the process-wide start method when
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processes are used. Set it to ``None`` to preserve Python's default or a
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method already selected by the embedding application.
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"""
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actor: str = "threads"
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learner: str = "threads"
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multiprocessing_context: str | None = "spawn"
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@dataclass
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@@ -91,7 +91,7 @@ from lerobot.robots import so_follower # noqa: F401
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from lerobot.teleoperators import gamepad, so_leader # noqa: F401
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from lerobot.teleoperators.utils import TeleopEvents
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from lerobot.utils.device_utils import get_safe_torch_device
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from lerobot.utils.process import ProcessSignalHandler, ensure_multiprocessing_start_method
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from lerobot.utils.process import ProcessSignalHandler
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from lerobot.utils.random_utils import set_seed
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from lerobot.utils.robot_utils import precise_sleep
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from lerobot.utils.transition import (
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@@ -124,7 +124,9 @@ def actor_cli(cfg: TrainRLServerPipelineConfig):
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cfg.validate()
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display_pid = False
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if not use_threads(cfg):
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ensure_multiprocessing_start_method(cfg.policy.concurrency.multiprocessing_context)
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import torch.multiprocessing as mp
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mp.set_start_method("spawn")
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display_pid = True
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# Create logs directory to ensure it exists
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@@ -102,7 +102,7 @@ from lerobot.utils.constants import (
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)
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from lerobot.utils.device_utils import get_safe_torch_device
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from lerobot.utils.io_utils import load_json, write_json
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from lerobot.utils.process import ProcessSignalHandler, ensure_multiprocessing_start_method
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from lerobot.utils.process import ProcessSignalHandler
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from lerobot.utils.random_utils import set_seed
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from lerobot.utils.utils import (
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format_big_number,
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@@ -123,7 +123,9 @@ def train_cli(cfg: TrainRLServerPipelineConfig):
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# Fail fast with a friendly error if the optional ``hilserl`` extra is missing.
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require_package("grpcio", extra="hilserl", import_name="grpc")
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if not use_threads(cfg):
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ensure_multiprocessing_start_method(cfg.policy.concurrency.multiprocessing_context)
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import torch.multiprocessing as mp
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mp.set_start_method("spawn")
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# Use the job_name from the config
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train(
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@@ -58,6 +58,9 @@ class BiSOFollower(BimanualMixin, Robot):
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port=config.left_arm_config.port,
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disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect,
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max_relative_target=config.left_arm_config.max_relative_target,
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position_p_coefficient=config.left_arm_config.position_p_coefficient,
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position_i_coefficient=config.left_arm_config.position_i_coefficient,
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position_d_coefficient=config.left_arm_config.position_d_coefficient,
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use_degrees=config.left_arm_config.use_degrees,
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cameras=left_arm_cameras,
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)
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@@ -68,6 +71,9 @@ class BiSOFollower(BimanualMixin, Robot):
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port=config.right_arm_config.port,
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disable_torque_on_disconnect=config.right_arm_config.disable_torque_on_disconnect,
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max_relative_target=config.right_arm_config.max_relative_target,
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position_p_coefficient=config.right_arm_config.position_p_coefficient,
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position_i_coefficient=config.right_arm_config.position_i_coefficient,
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position_d_coefficient=config.right_arm_config.position_d_coefficient,
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use_degrees=config.right_arm_config.use_degrees,
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cameras=config.right_arm_config.cameras,
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)
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@@ -41,6 +41,11 @@ class SOFollowerConfig:
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# Set to `True` for backward compatibility with previous policies/dataset
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use_degrees: bool = True
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# Position-mode PID gains written to Feetech STS3215 motors at connect time.
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position_p_coefficient: int = 16
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position_i_coefficient: int = 0
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position_d_coefficient: int = 32
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@RobotConfig.register_subclass("so101_follower")
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@RobotConfig.register_subclass("so100_follower")
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@@ -161,11 +161,9 @@ class SOFollower(Robot):
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self.bus.configure_motors()
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for motor in self.bus.motors:
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self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
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# Set P_Coefficient to lower value to avoid shakiness (Default is 32)
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self.bus.write("P_Coefficient", motor, 16)
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# Set I_Coefficient and D_Coefficient to default value 0 and 32
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self.bus.write("I_Coefficient", motor, 0)
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self.bus.write("D_Coefficient", motor, 32)
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self.bus.write("P_Coefficient", motor, self.config.position_p_coefficient)
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self.bus.write("I_Coefficient", motor, self.config.position_i_coefficient)
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self.bus.write("D_Coefficient", motor, self.config.position_d_coefficient)
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if motor == "gripper":
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self.bus.write("Max_Torque_Limit", motor, 500) # 50% of max torque to avoid burnout
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@@ -16,39 +16,11 @@
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# limitations under the License.
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import logging
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import multiprocessing
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import os
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import signal
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import sys
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def ensure_multiprocessing_start_method(start_method: str | None) -> None:
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"""Set a multiprocessing start method once, or verify the existing method matches.
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Passing ``None`` leaves Python's process-wide default untouched. This is useful
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when LeRobot is embedded in an application that owns multiprocessing setup.
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"""
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if start_method is None:
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return
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available_methods = multiprocessing.get_all_start_methods()
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if start_method not in available_methods:
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raise ValueError(
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f"Multiprocessing start method must be one of {available_methods} on this platform, "
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f"got {start_method!r}."
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)
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current_method = multiprocessing.get_start_method(allow_none=True)
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if current_method is None:
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multiprocessing.set_start_method(start_method)
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elif current_method != start_method:
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raise RuntimeError(
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f"Multiprocessing start method is already {current_method!r}; cannot change it to "
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f"{start_method!r}. Set the configured multiprocessing context to null to keep the "
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"application's existing method, or launch LeRobot in a fresh process."
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)
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class ProcessSignalHandler:
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"""Utility class to attach graceful shutdown signal handlers.
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@@ -113,7 +113,6 @@ def test_gaussian_actor_config_default_initialization():
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# Concurrency configuration
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assert config.concurrency.actor == "threads"
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assert config.concurrency.learner == "threads"
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assert config.concurrency.multiprocessing_context == "spawn"
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assert isinstance(config.actor_network_kwargs, ActorNetworkConfig)
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assert isinstance(config.policy_kwargs, PolicyConfig)
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@@ -153,7 +152,6 @@ def test_concurrency_config():
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config = ConcurrencyConfig()
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assert config.actor == "threads"
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assert config.learner == "threads"
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assert config.multiprocessing_context == "spawn"
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def test_gaussian_actor_config_custom_initialization():
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@@ -109,3 +109,22 @@ def test_send_action(follower):
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goal_pos = {m: (i + 1) * 10 for i, m in enumerate(follower.bus.motors)}
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follower.bus.sync_write.assert_called_once_with("Goal_Position", goal_pos)
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def test_configure_writes_position_pid_coefficients():
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bus_mock = _make_bus_mock()
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bus_mock.motors = ["shoulder_pan"]
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robot = MagicMock()
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robot.bus = bus_mock
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robot.config = SO100FollowerConfig(
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port="/dev/null",
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position_p_coefficient=32,
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position_i_coefficient=1,
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position_d_coefficient=16,
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)
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SO100Follower.configure(robot)
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bus_mock.write.assert_any_call("P_Coefficient", "shoulder_pan", 32)
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bus_mock.write.assert_any_call("I_Coefficient", "shoulder_pan", 1)
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bus_mock.write.assert_any_call("D_Coefficient", "shoulder_pan", 16)
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