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Author SHA1 Message Date
CarolinePascal 4f3f696b27 docs(motors): write the API reference docstrings
Takes src/lerobot/motors/ (excluding motors_bus.py, off-limits) to 100% public docstring coverage:
dynamixel, feetech, damiao, robstride, calibration_gui.py, and encoding_utils.py. Also fixes a handful of
pre-existing docstring bugs found along the way (a parameter name that didn't match its signature, missing
argument descriptions, a copy-paste typo).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
2026-08-07 00:42:11 +02:00
Pepijn 741005d719 docs: write the API reference docstrings
Every docstring change for the API reference, on top of the infrastructure PR
which contains none. Two halves: a repo-wide pass over what the renderer cannot
handle, and `src/lerobot/robots/` taken to 100% as the worked example.

**Renderer fixes, repo-wide.** Both of these render incorrectly the moment
`[[autodoc]]` is on, and both were verified against a local build:

- 24 Sphinx roles across three files. They are unsupported and render as literal
  `:pymeth:` text. Method references become doc-builder cross-references; the
  ones pointing at instance attributes become inline code, since attributes get
  no autodoc anchor and a cross-reference would be a dead link.
- 43 `Attributes:` sections across 27 files. doc-builder parses a bare
  `Attributes:` as a synonym for `Parameters:` — `Robot`'s attributes rendered
  inside `<paramsdesc>`, presenting `config_class` and `name` to readers as
  constructor arguments when the actual parameter is `config`. Where the
  original carried no type, the type comes from the real class annotation rather
  than being invented.

The four base classes every other module inherits from — `robot.py`,
`teleoperator.py`, `motors_bus.py`, `camera.py` — are rewritten to the standard,
since subclasses document only their deviations from that text.

Three docstring errors corrected in passing: `Teleoperator.get_action` pointed
at `observation_features`, which `Teleoperator` does not have; `send_feedback`
documented a `Returns:` for a method returning `None`; and `config_class` was
typed `RobotConfig` instead of `type[TeleoperatorConfig]`.

**`robots/`, 109/306 -> 306/306.** The configuration dataclasses were the
substantial part. Their fields were documented only with `#` comments above each
field, which doc-builder cannot see: before this, `SO101FollowerConfig` rendered
all eleven of its fields with not one description. Each config now carries an
`Args:` block on the concrete registered class, covering inherited fields too,
because doc-builder renders only a class's own docstring and several of these
configs are thin multiple-inheritance shims whose body is `pass`.

The inline comments are kept rather than removed, so fields stay annotated in
the source as well as on the rendered page. Note this leaves each field
described twice, and only the `Args:` block is checked against the signature by
`make check-docstrings`, so the two can drift.

Writing them turned up things worth stating plainly on the page rather than
leaving in a comment: which configs have no serial port at all because they talk
over a network or the cloud (Reachy 2, Unitree G1, LeKiwi's client, EarthRover),
which manage their own calibration so `calibration_dir` does nothing, that
OpenArm's default joint limits are deliberately tiny until `side` is set, and
that reBot's `port` means a different thing depending on `can_adapter`.

Two pre-existing docstring bugs that the doctest infrastructure surfaced are
fixed here: `SerialMotorsBus` used `>>>` inside a ```bash block to show CLI
output, which doctest read as Python and failed on with a SyntaxError, and
`MotorsBus.torque_disabled`'s example referenced an undefined name.
`ensure_safe_goal_position` gains a genuinely executing example so the doctest
gate is not vacuous.

**Gates ratcheted**, each of which the infrastructure PR left deliberately
loose:

- `check_docstrings.py`'s ignore list emptied — the ten objects it held all had
  bare `Attributes:` sections, now converted.
- `check_config_docstrings.py`'s ignore list emptied — every registered robot
  config documents its port and calibration semantics.
- `robots/` removed from the ruff `D` per-file-ignores, as are the two
  package-root files, whose one-line docstring issues are fixed here. D100 and
  D104 are ignored globally instead: they ask for a banner on every file and
  every `__init__.py`, which appears on no rendered page.
- `interrogate` raised 52 -> 55 against a measured 55.3%.
- The four `robots/` files carrying examples added to the doctest allowlist,
  which shipped empty.

Verified: all 66 changed files under `src/lerobot/` are provably docstring-only
(AST with docstrings stripped is byte-identical to main), no comment line is
removed anywhere in `robots/`, and 708 tests pass.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-06 21:01:34 +02:00
Pepijn 2e8345a5cc docs: add API documentation infrastructure
LeRobot's documentation build passes `--not_python_module`, which tells
doc-builder there is no importable Python package and disables `[[autodoc]]`
entirely. The result is that all 90+ pages are hand-written guides and there is
no generated API reference at all.

This is the machinery to change that. It deliberately contains no docstring
changes of its own — every docstring edit lives in the follow-up PR, so this
one can be reviewed as tooling and configuration alone.

**The standard.** `docs/source/writing_docstrings.mdx` is the contract: Google
section headers with Hugging Face type formatting, the machine-checked argument
line, `**Attributes**:`, doc-builder cross-references, fenced doctest examples.
It also records three behaviours that are not discoverable from the source and
were verified against a local build: `[[autodoc]]` silently skips members with
no docstring; doc-builder does not inherit docstrings from base classes, so a
registered config shim whose body is `pass` renders every field with no
description; and module-level aliases resolve to the canonical class.

**Autodoc turned on**, with two changes that are not obvious:

- `--version main` on the main-docs job. Without `--not_python_module`,
  doc-builder resolves the version from `lerobot.__version__` and only maps it
  to the default branch when it contains "dev". transformers relies on that;
  our main carries 0.6.2. Verified by building both ways — dropping the flag
  alone would publish the main docs to /lerobot/v0.6.2/ instead of
  /lerobot/main/ and disable notebook building.
- `pre_command` on both jobs. doc-builder ships a mock-deps registry entry for
  lerobot, so the reusable workflow takes its light-install path, which cannot
  import the package. The heavy dependencies cannot be mocked either: draccus
  runs `register_subclass` at import time and `processor/converters.py` calls
  `functools.singledispatch.register(torch.Tensor)`, which needs a real class.
  `[dataset]` is the only extra required.

Workflow triggers gain `src/**`, since the reference is now generated from
docstrings. `docs/source/api/` is excluded from the prettier hook, which reads
`[[autodoc]]` member lists as lazy paragraph continuations and joins a ten-entry
list onto one line.

Nine API reference pages, scaffolded with each module's base class.

**Doctests.** `LeRobotDocTestParser` is mandatory rather than optional here:
ruff's `docstring-code-format = true` drops the blank line before a closing
fence, after which stdlib's `_EXAMPLE_RE` reads the fence as expected output and
every example with output fails. It is written against the installed pytest
rather than copied from transformers, whose version predates pytest 9's
`import_path` signature and its own fix for the `@property` line-number bug.
`preprocess_string` also diverges: the upstream fenced-block split puts a
single-line example's code in a chunk with no `>>>` in it, so neither the CUDA
skip nor the `+IGNORE_RESULT` injection fires for it.

**Checkers.** `utils/check_docstrings.py` is the ~300-line core of the
2203-line transformers original; the `@auto_docstring` system, modular
propagation, GitPython and `checkers.py` are not ported.
`utils/check_config_docstrings.py` checks that every registered robot config
documents its port and calibration semantics.

**Gates**, all set to values that pass today: ruff `D` with per-file-ignores
per unconverted module, `interrogate` at `fail-under = 52` against a measured
52.1%, and Makefile targets wired into the quality workflow. The doctest
allowlist ships empty and the `doctest` target handles that, because the files
carrying runnable examples arrive with the docstring PR.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-06 20:57:26 +02:00
130 changed files with 4977 additions and 4149 deletions
+21 -5
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@@ -24,19 +24,24 @@ on:
required: false
type: string
# Triggers the workflow on push events to main for the docs folder
# Triggers on pushes to main that touch the docs or the sources the API reference is generated from.
# `src/**` is included because the API reference is built from docstrings via `[[autodoc]]`: without it,
# published API pages would go stale as soon as a docstring changed.
push:
branches:
- main
paths:
- "docs/**"
- "src/**"
# Triggers the workflow on pull request events targeting main for the docs folder
# Same for pull requests, so a docstring change gets a preview build and a broken `[[autodoc]]` path
# fails the PR rather than main.
pull_request:
branches:
- main
paths:
- "docs/**"
- "src/**"
release:
types: [published]
@@ -59,12 +64,21 @@ jobs:
with:
commit_sha: ${{ github.sha }}
package: lerobot
# doc-builder ships a mock-deps registry entry for lerobot, so the reusable workflow takes its
# "light install" path: `pip install ./lerobot --no-deps` plus a handful of real dependencies.
# That is not enough to import lerobot — draccus runs `register_subclass` at import time and
# `processor/converters.py` calls `functools.singledispatch.register(torch.Tensor)`, neither of
# which works against a mock. Install the package for real before the build.
pre_command: uv pip install "./lerobot[dataset]"
# `--version main` is load-bearing: without `--not_python_module`, doc-builder falls back to
# `lerobot.__version__` and only maps that to the default branch when it contains "dev". Our main
# branch carries a release version (0.6.2), so omitting this would publish the main docs to
# /lerobot/v0.6.2/ instead of /lerobot/main/ and disable notebook building.
additional_args: >-
--not_python_module
${{
(github.event_name == 'release' && format('--version {0}', github.event.release.tag_name)) ||
(inputs.version != '' && format('--version {0}', inputs.version)) ||
''
'--version main'
}}
secrets:
token: ${{ secrets.HUGGINGFACE_PUSH }}
@@ -83,4 +97,6 @@ jobs:
commit_sha: ${{ github.event.pull_request.head.sha }}
pr_number: ${{ github.event.number }}
package: lerobot
additional_args: --not_python_module
# See the comment on build_main_docs. The PR workflow passes its own `--version pr_<n>`, so no
# additional_args are needed here.
pre_command: uv pip install "./lerobot[dataset]"
+38
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@@ -56,3 +56,41 @@ jobs:
uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1
with:
extra_args: --all-files --show-diff-on-failure --color=always
# This job runs the examples in our docstrings and validates the doctest allowlist.
# See docs/source/writing_docstrings.mdx for the standard these enforce.
doc-checks:
name: Run Documentation Checks (Doctests)
runs-on: ubuntu-latest
env:
# Examples that need a physical robot, a serial port or a Hub download are skipped by content.
# Everything else has to actually run. See src/lerobot/utils/doctest_utils.py.
SKIP_HARDWARE_DOCTEST: "1"
SKIP_CUDA_DOCTEST: "1"
steps:
- name: Checkout code
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
persist-credentials: false
- name: Setup uv and Python
uses: astral-sh/setup-uv@11f9893b081a58869d3b5fccaea48c9e9e46f990 # v8.3.2
with:
enable-cache: true
version: "0.11.30"
python-version: "3.12"
- name: Install dependencies
run: uv sync --locked --extra test --extra dataset
- name: Check the doctest list is sorted and its paths exist
run: make check-doctest-list
- name: Check documented arguments match their signatures
run: make check-docstrings
- name: Check docstring coverage has not regressed
run: uv run --with interrogate interrogate --config=pyproject.toml
- name: Run doctests
run: make doctest
+11 -2
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@@ -67,7 +67,11 @@ repos:
args: [--prose-wrap=preserve]
# Jinja2 model-card templates use a .md extension but contain {% ... %} /
# {{ ... }} tags that prettier's Markdown formatter mangles (e.g. table loops).
exclude: ^src/lerobot/templates/.*\.md$
#
# docs/source/api/ holds the generated API reference. Its `[[autodoc]]` blocks restrict output
# to an indented `- member` list, which prettier reads as a lazy paragraph continuation and
# joins onto one line — silently turning a member list into part of the directive.
exclude: ^(src/lerobot/templates/.*\.md|docs/source/api/.*\.mdx)$
##### Security #####
- repo: https://github.com/gitleaks/gitleaks
@@ -104,8 +108,13 @@ repos:
# args: ["--docstring-style", "google", "-v", "2"]
# exclude: ^tests/.*$
# interrogate runs in CI (quality.yml, doc-checks job) rather than here. Its 1.7.0 release still imports
# the deprecated `py` package, which resolves against whatever `py` happens to be importable in
# pre-commit's isolated env — on a machine with miniconda on the path that is a stray `py.py` and the
# hook dies before it reads any config. The gate is the same either way; the CI step is just reliable.
# - repo: https://github.com/econchick/interrogate
# rev: 1.7.0
# hooks:
# - id: interrogate
# args: ["-vv", "--config=pyproject.toml"]
# args: ["--config=pyproject.toml"]
# pass_filenames: false
+4
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@@ -50,6 +50,10 @@ To run checks manually on all files:
pre-commit run --all-files
```
### Docstrings
The API reference is generated from the docstrings in `src/lerobot/`. If you add or change anything public, follow the [docstring standard](https://huggingface.co/docs/lerobot/writing_docstrings) — the format is parsed by the renderer and checked in CI.
### Running Tests
We use `pytest`. First, ensure you have test artifacts by installing **git-lfs**:
+26
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@@ -184,3 +184,29 @@ test-smolvla-ete-eval:
# backend, so it does not require a real model checkpoint or GPU.
annotation-e2e:
uv run python -m tests.annotations.run_e2e_smoke
# Docstring & doctest checks. See docs/source/writing_docstrings.mdx for the standard these enforce.
# Run the examples in the docstrings listed in utils/documentation_tests.txt. Hardware and GPU examples are
# skipped by content (see src/lerobot/utils/doctest_utils.py); CI sets both flags.
doctest:
@files=$$(grep -v '^\s*#' utils/documentation_tests.txt | grep -v '^\s*$$'); \
if [ -z "$$files" ]; then \
echo "utils/documentation_tests.txt lists no files; nothing to run."; \
else \
SKIP_HARDWARE_DOCTEST=1 uv run pytest --doctest-modules --no-header -q $$files; \
fi
check-doctest-list:
uv run python utils/check_doctest_list.py
fix-doctest-list:
uv run python utils/check_doctest_list.py --fix_and_overwrite
check-docstrings:
uv run python utils/check_docstrings.py
uv run python utils/check_config_docstrings.py
fix-docstrings:
uv run python utils/check_docstrings.py --fix_and_overwrite
uv run python utils/check_doctest_list.py --fix_and_overwrite
+60
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@@ -0,0 +1,60 @@
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Root conftest: makes doctest collection use LeRobot's parser.
This only affects `--doctest-modules` runs (see `make doctest`). The test suite itself is configured by
`tests/conftest.py`.
"""
import doctest
import _pytest.doctest
from lerobot.utils.doctest_utils import LeRobotDoctestModule, LeRobotDocTestParser
# Lets an example opt out of output comparison with `# doctest: +IGNORE_RESULT`, for calls whose output is
# a progress bar or otherwise not reproducible.
IGNORE_RESULT = doctest.register_optionflag("IGNORE_RESULT")
OutputChecker = doctest.OutputChecker
class CustomOutputChecker(OutputChecker):
"""An output checker that honours the `IGNORE_RESULT` flag."""
def check_output(self, want, got, optionflags):
"""Return `True` when `IGNORE_RESULT` is set, otherwise defer to stdlib.
Args:
want (`str`):
The expected output.
got (`str`):
The actual output.
optionflags (`int`):
Bitmask of active doctest option flags.
Returns:
`bool`: Whether the output is considered a match.
"""
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self, want, got, optionflags)
# Reassigning these module attributes is how doctest behaviour is customised; mypy sees it as assigning to
# a type, which is exactly what is intended here.
doctest.OutputChecker = CustomOutputChecker # type: ignore[misc]
_pytest.doctest.DoctestModule = LeRobotDoctestModule
doctest.DocTestParser = LeRobotDocTestParser # type: ignore[misc]
+22
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@@ -191,6 +191,28 @@
- sections:
- local: contributing
title: Contribute to LeRobot
- local: writing_docstrings
title: Writing docstrings
- local: backwardcomp
title: Backward compatibility
title: "About"
- sections:
- local: api/robots
title: Robots
- local: api/teleoperators
title: Teleoperators
- local: api/cameras
title: Cameras
- local: api/motors
title: Motors
- local: api/datasets
title: Datasets
- local: api/policies
title: Policies
- local: api/processor
title: Processors
- local: api/envs
title: Environments
- local: api/configs
title: Configuration
title: "API Reference"
+24
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@@ -0,0 +1,24 @@
# Cameras
Cameras supply the image observations a policy sees. Every backend — OpenCV, Intel RealSense, Reachy 2 —
implements the [`Camera`] interface, so swapping hardware does not change the code that reads frames.
See the [Cameras guide](../cameras) for choosing and configuring a camera, and
[Third-Party Cameras & Sensors](../third_party_sensors) for devices outside the core set.
## Camera
[[autodoc]] lerobot.cameras.Camera
- connect
- disconnect
- read
- async_read
- find_cameras
## CameraConfig
[[autodoc]] lerobot.cameras.CameraConfig
## make_cameras_from_configs
[[autodoc]] lerobot.cameras.make_cameras_from_configs
+27
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@@ -0,0 +1,27 @@
# Configuration
LeRobot configuration is plain dataclasses parsed by [draccus](https://github.com/dlwh/draccus), so every
field is settable from the CLI. [`TrainPipelineConfig`] is the top-level object for `lerobot-train`.
Polymorphic configs (policies, robots, environments) use `draccus.ChoiceRegistry`: a subclass registers
itself with `@register_subclass("name")` and is then selectable by that name on the command line.
## TrainPipelineConfig
[[autodoc]] lerobot.configs.train.TrainPipelineConfig
## PreTrainedConfig
[[autodoc]] lerobot.configs.PreTrainedConfig
## DatasetConfig
[[autodoc]] lerobot.configs.DatasetConfig
## EvalConfig
[[autodoc]] lerobot.configs.EvalConfig
## WandBConfig
[[autodoc]] lerobot.configs.WandBConfig
+23
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@@ -0,0 +1,23 @@
# Datasets
[`LeRobotDataset`] is the format every LeRobot script reads and writes. It is episode-aware, decodes video
observations on the fly, and round-trips to the Hugging Face Hub.
See [Using LeRobotDataset](../lerobot-dataset-v3) for the format and the common operations,
[Porting Large Datasets](../porting_datasets_v3) for migration, and [Tools](../tools) for the CLI.
## LeRobotDataset
[[autodoc]] lerobot.datasets.LeRobotDataset
## LeRobotDatasetMetadata
[[autodoc]] lerobot.datasets.LeRobotDatasetMetadata
## MultiLeRobotDataset
[[autodoc]] lerobot.datasets.MultiLeRobotDataset
## StreamingLeRobotDataset
[[autodoc]] lerobot.datasets.StreamingLeRobotDataset
+19
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@@ -0,0 +1,19 @@
# Environments
Simulation environments are configured through [`EnvConfig`] and built by [`make_env`]. Each subclass
declares its `gym_kwargs` and how to construct the vectorised environments.
See [Environments from the Hub](../envhub) for using published environments and
[Adding a New Benchmark](../adding_benchmarks) for contributing one.
## EnvConfig
[[autodoc]] lerobot.envs.EnvConfig
## make_env
[[autodoc]] lerobot.envs.make_env
## make_env_config
[[autodoc]] lerobot.envs.make_env_config
+76
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@@ -0,0 +1,76 @@
# Motors
`MotorsBus` is the low-level interface to a chain of servos on a serial bus. Robots use it to read positions
and write goal positions; you rarely touch it directly unless you are adding hardware.
See [Bring Your Own Hardware](../integrate_hardware) for adding a new bus, and
[Updating Feetech Firmware](../feetech) and [Damiao Motors and CAN Bus](../damiao) for device-specific notes.
## MotorsBus
[[autodoc]] lerobot.motors.motors_bus.MotorsBus
## Motor
[[autodoc]] lerobot.motors.Motor
## MotorCalibration
[[autodoc]] lerobot.motors.MotorCalibration
## MotorNormMode
[[autodoc]] lerobot.motors.MotorNormMode
## DynamixelMotorsBus
[[autodoc]] lerobot.motors.dynamixel.DynamixelMotorsBus
- all
- is_calibrated
## Dynamixel OperatingMode
[[autodoc]] lerobot.motors.dynamixel.OperatingMode
## Dynamixel DriveMode
[[autodoc]] lerobot.motors.dynamixel.DriveMode
## Dynamixel TorqueMode
[[autodoc]] lerobot.motors.dynamixel.TorqueMode
## FeetechMotorsBus
[[autodoc]] lerobot.motors.feetech.FeetechMotorsBus
- all
- is_calibrated
## Feetech OperatingMode
[[autodoc]] lerobot.motors.feetech.OperatingMode
## Feetech DriveMode
[[autodoc]] lerobot.motors.feetech.DriveMode
## Feetech TorqueMode
[[autodoc]] lerobot.motors.feetech.TorqueMode
## DamiaoMotorsBus
CAN-based bus for Damiao arms. Motors must be switched to MIT control mode to be compatible.
[[autodoc]] lerobot.motors.damiao.DamiaoMotorsBus
- all
- is_connected
- is_calibrated
## RobstrideMotorsBus
CAN-based bus for Robstride motors. Motors must be switched to MIT control mode to be compatible.
[[autodoc]] lerobot.motors.robstride.RobstrideMotorsBus
- all
- is_connected
+20
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@@ -0,0 +1,20 @@
# Policies
Every policy inherits [`PreTrainedPolicy`], which combines a `torch.nn.Module` with the Hub mixin, so any
policy can be pushed to and loaded from the Hugging Face Hub with the same two calls.
Each policy has its own guide with training recipes and results — [ACT](../act), [SmolVLA](../smolvla),
[π₀](../pi0), [π₀.₅](../pi05) and the rest are listed under Policies. To add one, see
[Adding a Policy](../bring_your_own_policies).
## PreTrainedPolicy
[[autodoc]] lerobot.policies.pretrained.PreTrainedPolicy
## PreTrainedConfig
[[autodoc]] lerobot.configs.PreTrainedConfig
## make_policy
[[autodoc]] lerobot.policies.factory.make_policy
+20
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@@ -0,0 +1,20 @@
# Processors
Processors are the data transformation layer between a robot, a dataset and a policy. A pipeline is a chain
of [`ProcessorStep`]s; each step declares how it transforms both the data and the feature contract.
See [Introduction to Robot Processors](../introduction_processors) for the concepts,
[Implement your own processor](../implement_your_own_processor) to write a step, and
[Debug your processor pipeline](../debug_processor_pipeline) when a pipeline misbehaves.
## ProcessorStep
[[autodoc]] lerobot.processor.pipeline.ProcessorStep
## DataProcessorPipeline
[[autodoc]] lerobot.processor.pipeline.DataProcessorPipeline
## PolicyProcessorPipeline
[[autodoc]] lerobot.processor.pipeline.PolicyProcessorPipeline
+147
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@@ -0,0 +1,147 @@
# Robots
Every robot in LeRobot implements the [`Robot`] interface: connect, read an observation, send an action,
disconnect. Writing a policy or a recording script against that interface means it works with any supported
arm without change.
This page is the generated reference. For wiring, calibration and first-run instructions, start with the
hardware guides — [SO-101](../so101), [LeKiwi](../lekiwi), [Hope Jr](../hope_jr), [Reachy 2](../reachy2),
[OpenArm](../openarm) — or [Imitation Learning for Robots](../il_robots) for the end-to-end workflow. To add
a robot of your own, see [Bring Your Own Hardware](../integrate_hardware).
## Robot
The abstract base class. Subclasses implement every method below; the contract described here is what a
policy or recording loop can rely on.
[[autodoc]] lerobot.robots.Robot
- connect
- disconnect
- configure
- calibrate
- get_observation
- send_action
- observation_features
- action_features
- is_connected
- is_calibrated
## RobotConfig
[[autodoc]] lerobot.robots.RobotConfig
## make_robot_from_config
[[autodoc]] lerobot.robots.make_robot_from_config
## SO-100 and SO-101 followers
`SO100Follower` and `SO101Follower` are aliases of the same `SOFollower` class; the two arms differ in their
configuration, not their control code. `SO100FollowerConfig` and `SO101FollowerConfig` are likewise aliases
of `SOFollowerRobotConfig`.
[[autodoc]] lerobot.robots.so_follower.SOFollower
- all
[[autodoc]] lerobot.robots.so_follower.SOFollowerRobotConfig
## BiSOFollower
Two SO followers driven as one bimanual robot.
[[autodoc]] lerobot.robots.bi_so_follower.BiSOFollower
- all
[[autodoc]] lerobot.robots.bi_so_follower.BiSOFollowerConfig
## KochFollower
[[autodoc]] lerobot.robots.koch_follower.KochFollower
- all
[[autodoc]] lerobot.robots.koch_follower.KochFollowerConfig
## LeKiwi
`LeKiwi` runs on the robot itself. `LeKiwiClient` is the host-side proxy that talks to it over the network
and presents the same [`Robot`] interface.
[[autodoc]] lerobot.robots.lekiwi.LeKiwi
- all
[[autodoc]] lerobot.robots.lekiwi.LeKiwiConfig
[[autodoc]] lerobot.robots.lekiwi.LeKiwiClient
- all
[[autodoc]] lerobot.robots.lekiwi.LeKiwiClientConfig
## OpenArmFollower
[[autodoc]] lerobot.robots.openarm_follower.OpenArmFollower
- all
[[autodoc]] lerobot.robots.openarm_follower.OpenArmFollowerConfig
## BiOpenArmFollower
[[autodoc]] lerobot.robots.bi_openarm_follower.BiOpenArmFollower
- all
[[autodoc]] lerobot.robots.bi_openarm_follower.BiOpenArmFollowerConfig
## OmxFollower
[[autodoc]] lerobot.robots.omx_follower.OmxFollower
- all
[[autodoc]] lerobot.robots.omx_follower.OmxFollowerConfig
## Reachy2Robot
[[autodoc]] lerobot.robots.reachy2.Reachy2Robot
- all
[[autodoc]] lerobot.robots.reachy2.Reachy2RobotConfig
## UnitreeG1
[[autodoc]] lerobot.robots.unitree_g1.UnitreeG1
- all
[[autodoc]] lerobot.robots.unitree_g1.UnitreeG1Config
## Hope Jr
The Hope Jr humanoid is exposed as two independent robots, an arm and a hand.
[[autodoc]] lerobot.robots.hope_jr.HopeJrArm
- all
[[autodoc]] lerobot.robots.hope_jr.HopeJrArmConfig
[[autodoc]] lerobot.robots.hope_jr.HopeJrHand
- all
[[autodoc]] lerobot.robots.hope_jr.HopeJrHandConfig
## RebotB601Follower
[[autodoc]] lerobot.robots.rebot_b601_follower.RebotB601Follower
- all
[[autodoc]] lerobot.robots.rebot_b601_follower.RebotB601FollowerRobotConfig
## BiRebotB601Follower
[[autodoc]] lerobot.robots.bi_rebot_b601_follower.BiRebotB601Follower
- all
[[autodoc]] lerobot.robots.bi_rebot_b601_follower.BiRebotB601FollowerConfig
## EarthRoverMiniPlus
[[autodoc]] lerobot.robots.earthrover_mini_plus.EarthRoverMiniPlus
- all
[[autodoc]] lerobot.robots.earthrover_mini_plus.EarthRoverMiniPlusConfig
+30
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@@ -0,0 +1,30 @@
# Teleoperators
A teleoperator produces actions for a robot to follow — a leader arm, a gamepad, a keyboard, a phone. All of
them implement the [`Teleoperator`] interface, so a recording script written against it works with any input
device.
See [Phone teleoperation](../phone_teleop) and [Isaac Teleop](../isaac_teleop) for setup guides, and
[Imitation Learning for Robots](../il_robots) for the recording workflow.
## Teleoperator
[[autodoc]] lerobot.teleoperators.Teleoperator
- connect
- disconnect
- configure
- calibrate
- get_action
- send_feedback
- action_features
- feedback_features
- is_connected
- is_calibrated
## TeleoperatorConfig
[[autodoc]] lerobot.teleoperators.TeleoperatorConfig
## make_teleoperator_from_config
[[autodoc]] lerobot.teleoperators.make_teleoperator_from_config
+1 -4
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@@ -62,10 +62,7 @@ Reference data points on a 4×H100 80 GB cluster (`accelerate launch --num_proce
| `smolvla` | 27m 49s | 0.312 | 0.011 | ~80% | `--policy.path=lerobot/smolvla_base`, `freeze_vision_encoder=false`, `train_expert_only=false` |
| `pi05` | 3h 41m | 2.548 | 0.014 | ~95% | `--policy.pretrained_path=lerobot/pi05_base`, `gradient_checkpointing=true`, `dtype=bfloat16`, vision encoder + expert trained |
Training logs separate the full iteration into `dataloading_s` (`next(dl_iter)`), `preprocessing_s`
(image conversion and the policy pipeline), and `update_s` (the optimizer update). `step_s` covers all
three and drives `samples_per_s`. The benchmark above predates this split, so its `dataloading_s` includes
preprocessing.
The `dataloading_s` vs. `update_s` ratio is the diagnostic that matters: when `dataloading_s` approaches `update_s`, more GPUs stop helping — your dataloader is the bottleneck and you should look at `--num_workers`, image resolution, and disk speed before adding compute.
### Schedule and checkpoints
+16 -81
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@@ -241,89 +241,24 @@ See the [Real-Time Chunking](./rtc) guide for details on tuning RTC parameters.
---
## Interactive Sessions
Add `--interactive=true` to drive the rollout from the terminal instead of starting immediately. Hardware connects and the policy loads as usual, but **the robot stays still until you type `/start`** — useful when you want to position the scene first, re-instruct the policy between attempts, or run several takes without paying the load time again.
```bash
lerobot-rollout \
--strategy.type=base \
--policy.path=${HF_USER}/my_smolvla_policy \
--robot.type=so100_follower \
--robot.port=/dev/ttyACM0 \
--robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
--task="pick up the cube" \
--interactive=true
```
| Command | Action |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `/start` | Start (or restart) the policy control loop |
| `/subtask <text>` | Change the instruction the policy follows, without stopping. No argument prints the current task. Only affects policies that condition on language (SmolVLA, π0/π0.5, and similar) |
| `/ask <question>` | Ask a supported policy text head about its latest view. The answer is generated in the background without pausing the session |
| `/reset` | Stop movement, return the robot to its startup position, and restore the `--task` instruction |
| `/stop` | End the session and run the normal shutdown routines |
| `/help` | List the commands |
```text
> /start
Rollout running — task 'pick up the cube'. /subtask <text> to change it, ...
> /subtask put the cube in the box
Task: 'pick up the cube' → 'put the cube in the box' (applies from the next policy inference)
> /ask where is the red cube?
Question queued: 'where is the red cube?' (the rollout keeps running)
[policy] The red cube is beside the bowl.
> /reset
Task restored to 'pick up the cube'
Resetting — returning the robot to its initial position...
Robot reset — holding at initial position. /start to run.
> /stop
```
`Ctrl-C` still shuts down as usual, and closing stdin (`Ctrl-D`, or the end of a piped script) ends the session — so a piped script must keep stdin open for the intended duration:
```bash
(printf '/start\n'; sleep 60; printf '/stop\n') | lerobot-rollout ... --interactive=true
```
**How `/subtask` reaches the policy.** The stdin reader publishes the new instruction to the inference engine, which picks it up on its own inference thread, so nothing is mutated across threads while the robot is moving. How quickly the behavior changes depends on the backend:
- **Sync** (`--inference.type=sync`) — precomputed chunk actions are dropped, so the new instruction applies on the very next control tick. Without this a chunking policy would keep executing up to `chunk_size` stale actions (seconds of the old behavior). Only the queued actions are discarded, so observation history and the rest of the episode state are preserved.
- **RTC** (`--inference.type=rtc`) — the next chunk is generated under the new instruction and merged over the previous chunk's leftover prefix, so the switch lands within one inference and the motion stays continuous. The queue is deliberately not cleared: that would leave the robot without commands for a full inference latency. (With blending turned off via `--inference.rtc.enabled=false` the queued chunk drains first, so the switch lands up to one chunk later.)
With `--use_torch_compile=true`, a switch whose instruction tokenizes to a different length can trigger a recompilation on the next forward pass, pausing inference for as long as the original warm-up took. Prefer leaving compilation off for sessions where you expect to re-instruct the policy often.
**How `/ask` runs without taking over the rollout.** During an active rollout, the inference engine caches the latest policy-ready observation, so the command reader never touches cameras, processors, or robot hardware. A single background worker sends that snapshot to the optional `PreTrainedPolicy.generate_text(..., kind=TextKind.VQA, user_text=question)` hook and prints the result when ready. Questions are independent turns; there is no conversation history, and a second question is rejected while one is running so stale image tensors cannot accumulate. WALL-OSS (`wall_x`) is the first policy implementing this hook; policies without a compatible text head report that `/ask` is unsupported.
Text and action calls share one policy safely: the engine gives a pending question priority after the current action inference finishes, while action inference uses a non-blocking gate. The hardware loop therefore keeps ticking and `/ask` never clears an action queue. RTC continues dispatching its buffered actions while text is decoded. Sync keeps the robot on its last commanded target until the policy is available again. Text generation still consumes model/GPU capacity, so response generation can reduce action freshness; RTC is preferred when uninterrupted action buffering matters.
`/stop` suppresses any late answer and gives an active decoder five seconds to finish cleanly. If it is stuck, hardware teardown continues rather than leaving the robot session open indefinitely; the daemon may retain its model/GPU resources until it returns.
**Console logs are muted while the session runs** so they don't interleave with what you're typing; they resume when it ends. A fatal inference error is still printed. Run without `--interactive` to watch the live log.
Interactive sessions currently require `--strategy.type=base`: the recording strategies finalize their dataset when their loop exits, so they cannot be restarted by `/start`, and their keyboard controls would compete for the same terminal.
---
## Common Flags
| Flag | Description | Default |
| --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| `--policy.path` | **Required.** HF Hub model ID or local checkpoint path | -- |
| `--robot.type` | **Required.** Robot type (e.g. `so100_follower`, `koch_follower`) | -- |
| `--robot.port` | Serial port for the robot | -- |
| `--robot.cameras` | Camera configuration (JSON dict) | -- |
| `--fps` | Control loop frequency | 30 |
| `--duration` | Run time in seconds (0 = infinite) | 0 |
| `--device` | Torch device (`cpu`, `cuda`, `mps`) | auto |
| `--task` | Task description (used when no dataset is provided) | -- |
| `--display_data` | Stream telemetry to Rerun visualization | false |
| `--display_ip` / `--display_port` | Remote Rerun server address | -- |
| `--interpolation_multiplier` | Action interpolation factor | 1 |
| `--interactive` | Chat-style stdin session (see [Interactive Sessions](#interactive-sessions)); the robot stays idle until `/start`. Base strategy only | false |
| `--use_torch_compile` | Enable `torch.compile` for inference | false |
| `--resume` | Resume a previous recording session | false |
| `--play_sounds` | Vocal synthesis for events | true |
| Flag | Description | Default |
| --------------------------------- | ----------------------------------------------------------------- | ------- |
| `--policy.path` | **Required.** HF Hub model ID or local checkpoint path | -- |
| `--robot.type` | **Required.** Robot type (e.g. `so100_follower`, `koch_follower`) | -- |
| `--robot.port` | Serial port for the robot | -- |
| `--robot.cameras` | Camera configuration (JSON dict) | -- |
| `--fps` | Control loop frequency | 30 |
| `--duration` | Run time in seconds (0 = infinite) | 0 |
| `--device` | Torch device (`cpu`, `cuda`, `mps`) | auto |
| `--task` | Task description (used when no dataset is provided) | -- |
| `--display_data` | Stream telemetry to Rerun visualization | false |
| `--display_ip` / `--display_port` | Remote Rerun server address | -- |
| `--interpolation_multiplier` | Action interpolation factor | 1 |
| `--use_torch_compile` | Enable `torch.compile` for inference | false |
| `--resume` | Resume a previous recording session | false |
| `--play_sounds` | Vocal synthesis for events | true |
---
-11
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@@ -242,17 +242,6 @@ python src/lerobot/scripts/augment_dataset_quantile_stats.py \
--repo-id=your_dataset
```
Recording, resuming, and merging aggregate quantiles from per-episode summaries, so `meta/stats.json` ends up holding a conservative envelope (`min` for `q <= 50`, `max` for `q > 50`) rather than whole-dataset quantiles. To estimate the latter, scan every episode with a running histogram:
```bash
python src/lerobot/scripts/augment_dataset_quantile_stats.py \
--repo-id=your_dataset \
--overwrite \
--skip-images
```
`--skip-images` keeps the existing image statistics and avoids video decoding when only `STATE`/`ACTION` need recomputing, and `--root` reads a local dataset instead of the Hub. These values are histogram estimates, subject to discretization and rebinning error, so they can differ from the conservative ones — which changes MolmoAct2's normalized targets and therefore its loss scale. Statistics already saved inside an existing checkpoint are not affected.
Alternatively, train MolmoAct2 with mean/std normalization:
```bash
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@@ -127,17 +127,6 @@ lerobot-edit-dataset \
Or keep the dataset as-is and pass `--policy.normalization_mapping='{"ACTION": "MEAN_STD", "STATE": "MEAN_STD", "VISUAL": "IDENTITY"}'`.
Recording, resuming, and merging aggregate quantiles from per-episode summaries, so `meta/stats.json` ends up holding a conservative envelope (`min` for `q <= 50`, `max` for `q > 50`) rather than whole-dataset quantiles. To estimate the latter, scan every episode with a running histogram:
```bash
python src/lerobot/scripts/augment_dataset_quantile_stats.py \
--repo-id=your_dataset \
--overwrite \
--skip-images
```
`--skip-images` keeps the existing image statistics and avoids video decoding when only `STATE`/`ACTION` need recomputing, and `--root` reads a local dataset instead of the Hub. These values are histogram estimates, subject to discretization and rebinning error, so they can differ from the conservative ones — which changes π₀.₅'s normalized targets and therefore its loss scale. Statistics already saved inside an existing checkpoint are not affected.
### Training Command Example
The same finetune with the VLM frozen: less memory, at some cost in success rate. Swap `--dataset.repo_id` for your own dataset.
-19
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@@ -2,25 +2,6 @@
https://diffusion-policy.cs.columbia.edu
## Training
The reference implementation maintains an exponential moving average (EMA) of the policy weights during training and evaluates the EMA weights. To reproduce this behavior, enable the trainer's EMA shadow:
```bash
lerobot-train \
--policy.type=diffusion \
--ema.enable=true \
...
```
Checkpoints then contain a directly loadable copy of the EMA weights next to the live ones, e.g. for evaluation:
```bash
lerobot-eval --policy.path=outputs/train/.../checkpoints/last/pretrained_model_ema ...
```
The EMA decay schedule (`--ema.inv_gamma`, `--ema.power`, ...) defaults to the reference implementation's values. For a constant decay instead of the warmup schedule (e.g. to match openpi's pi0/pi05 training), set `--ema.decay=0.99`.
## Citation
```bibtex
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@@ -59,22 +59,6 @@ When `use_relative_actions=true`, the training script automatically:
---
## EMA of the policy weights
OpenPI maintains an exponential moving average of the weights during training (`ema_decay=0.99` by default) and keeps the EMA copy for inference. To reproduce this with the LeRobot trainer, enable the EMA shadow with a constant decay:
```bash
python -m lerobot.scripts.lerobot_train \
--policy.type=pi05 \
--dataset.repo_id=your_org/your_dataset \
--ema.enable=true \
--ema.decay=0.99
```
Checkpoints then contain a directly loadable copy of the EMA weights in `pretrained_model_ema/` next to the live ones. Note that the shadow is a full extra copy of the parameters on the GPU. Like OpenPI (which disables EMA in its LoRA configs), EMA is not supported together with PEFT adapters.
---
## Citation
If you use this work, please cite both **OpenPI** and the π₀.₅ paper:
+287
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@@ -0,0 +1,287 @@
# Writing docstrings
LeRobot's API reference is generated directly from the docstrings in `src/lerobot/`. A docstring is not a
comment — it is the published documentation for that object, and the format below is what the renderer and
the CI checks parse.
This page is the contract. If you are adding or editing anything public in `src/lerobot/`, follow it.
> [!IMPORTANT]
> **An undocumented public method is an invisible one.** `[[autodoc]]` silently skips members that have no
> docstring — no warning, no error, it simply does not appear on the rendered page. Coverage and
> API-reference completeness are the same problem.
## The format in one example
Google section headers, Hugging Face type formatting. Both, not one or the other.
````python
def send_action(self, action: RobotAction, rate_hz: float = 30.0) -> RobotAction:
"""Command the robot to move to a target joint configuration.
Values are clipped by the configured maximum relative target before reaching the motors, so the
returned action may differ from the requested one.
Args:
action (`dict[str, float]`):
Target values keyed by motor name, e.g. `{"shoulder_pan.pos": 0.0}`. Keys must match the
robot's action features.
rate_hz (`float`, *optional*, defaults to `30.0`):
Control loop frequency.
Returns:
`dict[str, float]`: The action actually written to the motors after safety clipping.
Raises:
DeviceNotConnectedError: If the robot has not been connected.
Example:
```python
>>> from lerobot.robots.so_follower import SO101Follower, SO101FollowerConfig
>>> robot = SO101Follower(SO101FollowerConfig(port="/dev/ttyACM0")) # doctest: +SKIP
>>> robot.connect() # doctest: +SKIP
>>> robot.send_action({"shoulder_pan.pos": 0.0}) # doctest: +SKIP
```
"""
````
Cross-references are omitted from the examples on this page — see [Cross-references](#cross-references) for
their syntax and why they cannot be shown inside a code block.
## Rules
### Sections
`Args:` · `Returns:` · `Raises:` · `Yields:` · `Example:` · `Note:`
In that order. No other section headers. A one-line summary comes first, then an optional free-form
description, then the sections.
### The `Args:` line is machine-parsed
```
name (`type`, *optional*, defaults to `X`):
Description, indented on its own line.
```
The `*optional*, defaults to` clause is **checked against the real signature default** by
`make check-docstrings`. It is not decorative — if you write a default that has drifted from the code, CI
fails. Omit the clause entirely for required parameters:
```python
Args:
port (`str`):
Serial port the arm is connected to, e.g. `/dev/ttyACM0`.
max_relative_target (`float | dict[str, float]`, *optional*):
Caps the magnitude of the relative positional target vector. `None` disables clipping.
use_degrees (`bool`, *optional*, defaults to `True`):
Keep `True` for backward compatibility with existing policies and datasets.
```
Types go in backticks. Use `*optional*` with no `defaults to` when the default is `None` or is otherwise not
worth restating.
### `Returns:` is type-first
One indented line, type first, then a colon, then the description:
```python
Returns:
`dict[str, float]`: The action actually written to the motors after safety clipping.
```
`Yields:` takes the same shape.
### `**Attributes**:`, never `Attributes:`
doc-builder parses a bare `Attributes:` as a **synonym for `Parameters:`**, so your attributes get rendered
as constructor arguments. This is silent and wrong. Whenever the attributes differ from the constructor
parameters, use the bold form with a `--` separator:
```python
class Robot(abc.ABC):
"""The base abstract class for all LeRobot-compatible robots.
**Attributes**:
- **config_class** (`type[RobotConfig]`) -- The expected configuration class for this robot.
- **name** (`str`) -- The unique robot name used to identify this robot type.
"""
```
Note `--`, not `:`.
### Cross-references
Use doc-builder's bracket syntax: a square-bracketed backtick-quoted path. **Sphinx roles (`:pymeth:`,
`:pyattr:`) are not supported** and render as literal text on the page.
| Want | Write |
| ---------------------------- | ----------------------------------- |
| Class in the main package | &#91;`Robot`&#93; |
| Method, show the full path | &#91;`Robot.connect`&#93; |
| Method, show the bare name | &#91;`~Robot.connect`&#93; |
| Nested path | &#91;`~robots.Robot.connect`&#93; |
| Object in another HF library | &#91;`~accelerate.Accelerator`&#93; |
The `~` strips the path from the **link text only**; the link still resolves to the full path.
> [!NOTE]
> doc-builder resolves this syntax everywhere in a page — including inside fenced code blocks. That is why
> the docstring examples on this page use plain prose instead of cross-references: a code block containing
> one would render the resolved link rather than the syntax you need to type. In your own docstrings, use
> cross-references freely; this restriction only affects documentation _about_ the syntax.
### Callouts
Use GitHub-style blockquotes:
```markdown
> [!TIP]
> Call this once at startup — it takes about two seconds.
> [!WARNING]
> Torque is disabled on disconnect. The arm will drop if it is holding a load.
```
The `<Tip>` component is legacy per doc-builder; don't add new ones.
### Examples must be fenced
An example lives inside a fenced ` ```python ` block containing `>>> `. The fence is what makes it render
as a code block, and it is what the doctest preprocessor's regex looks for:
````python
Example:
```python
>>> from lerobot.robots.so_follower import SO101FollowerConfig
>>> cfg = SO101FollowerConfig(port="/dev/ttyACM0")
>>> cfg.use_degrees
True
```
````
> [!WARNING]
> An unfenced `>>>` is still collected — doctest finds prompts anywhere in a docstring. What you lose is the
> rendering, so it shows up as a wall of prose on the page. Every example needs the fence.
Every example either executes in CI or carries `# doctest: +SKIP`. Anything that touches hardware, a GPU, or
downloads from the Hub gets `+SKIP`:
````python
Example:
```python
>>> robot.connect() # doctest: +SKIP
>>> policy = ACTPolicy.from_pretrained("lerobot/act_aloha_sim_transfer_cube_human") # doctest: +SKIP
```
````
Add files containing runnable examples to `utils/documentation_tests.txt`.
Put examples on the three to five genuine entry points of a module. Examples on trivial accessors are noise.
## Three patterns you will hit constantly
### Config dataclasses
Configuration fields are historically documented with `#` comments above each field. **doc-builder cannot
see inline comments** — such a class renders with every field listed and not a single description. Move them
into an `Args:` block on the class docstring:
```python
@dataclass
class SOFollowerConfig:
"""Configuration for SO-family follower arms.
Args:
port (`str`):
Serial port the arm is connected to, e.g. `/dev/ttyACM0`.
max_relative_target (`float | dict[str, float]`, *optional*):
Caps the magnitude of the relative positional target vector. A scalar applies to all motors;
a dict maps motor name to a per-motor cap. `None` disables clipping.
use_degrees (`bool`, *optional*, defaults to `True`):
Keep `True` for backward compatibility with existing policies and datasets.
"""
port: str
max_relative_target: float | dict[str, float] | None = None
use_degrees: bool = True
```
> [!IMPORTANT]
> **doc-builder does not inherit docstrings from base classes.** LeRobot's registered config classes are
> often thin multiple-inheritance shims:
>
> ```python
> @RobotConfig.register_subclass("so101_follower")
> @dataclass
> class SOFollowerRobotConfig(RobotConfig, SOFollowerConfig):
> pass
> ```
>
> That class renders **every** field — including the ones it inherits — with no descriptions at all, no
> matter how well the bases are documented. The `Args:` block must live on the concrete class that
> `[[autodoc]]` names, and it must cover inherited fields too.
### Base class, then concrete subclass
The abstract base carries the canonical contract. Subclasses document only what deviates — port semantics,
calibration quirks, motor layout, supported feature keys. Do not copy the base contract into every subclass.
`Robot`, `Teleoperator`, `Camera`, `MotorsBus`, `ProcessorStep`, and `PreTrainedPolicy` all follow this
shape.
### Module-level aliases
Several public names are aliases rather than distinct classes:
```python
SO100FollowerConfig = SOFollowerRobotConfig
SO101FollowerConfig = SOFollowerRobotConfig
```
`[[autodoc]]` resolves the alias and renders the **canonical** class name, so a `## SO101FollowerConfig`
heading will show `class lerobot.robots.so_follower.SOFollowerRobotConfig` in the body. Document the
canonical class once, and mention the aliases in the page's prose rather than giving each alias its own
autodoc block.
## What not to document
- **Private members.** Anything starting with `_` is not part of the public API.
- **The type annotation restated as prose.** `port (`str`): A string.` adds nothing. Say what it is for.
- **Vendored upstream code.** `src/lerobot/policies/molmoact2/molmoact2_hf_model/` is vendored from
`transformers` and already carries upstream-style docstrings. Leave it alone — restyling it only creates
conflicts on the next sync. It is excluded from the API reference and from the docstring checks.
## How this is enforced
| Check | What it catches |
| ------------------------- | ---------------------------------------------------------------------------------------------------------- |
| `make check-docstrings` | An `Args:` entry that doesn't match the signature; a documented default that has drifted from the real one |
| `make doctest` | Examples that no longer run |
| `make check-doctest-list` | Stale or unsorted entries in `utils/documentation_tests.txt` |
| `ruff` (`D` rules) | Google-convention style violations |
| `interrogate` | Docstring coverage falling below the current threshold |
| doc-builder | A `[[autodoc]]` path that points at something that doesn't exist — this breaks the docs build |
Run them together before opening a PR:
```bash
make check-docstrings && make doctest && pre-commit run --all-files
```
Then render the page and actually look at it:
```bash
doc-builder build lerobot docs/source/ --build_dir /tmp/doc-build
```
## Checklist
- [ ] Every public member you touched has a docstring.
- [ ] Every `Args:` entry matches the signature, including the `*optional*, defaults to` clause.
- [ ] `Returns:` is type-first on one indented line.
- [ ] No bare `Attributes:` — use `**Attributes**:` with `--` separators.
- [ ] No Sphinx roles — cross-references use &#91;`~module.Class.method`&#93;.
- [ ] Examples are inside a fenced ` ```python ` block, and either run in CI or carry `# doctest: +SKIP`.
- [ ] Config dataclass fields are in an `Args:` block on the concrete class, not `#` comments.
- [ ] The rendered page has been eyeballed.
+65 -17
View File
@@ -401,19 +401,64 @@ exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py"]
# N: pep8-naming
# TODO: Uncomment rules when ready to use
select = [
"E", "W", "F", "I", "B", "C4", "T20", "N", "UP", "SIM" #, "A", "S", "D", "RUF"
"E", "W", "F", "I", "B", "C4", "T20", "N", "UP", "SIM", "D" #, "A", "S", "RUF"
]
ignore = [
"E501", # Line too long
"T201", # Print statement found
"T203", # Pprint statement found
"B008", # Perform function call in argument defaults
# D100/D104: module- and package-level docstrings. The API reference is generated from class and
# function docstrings; a banner at the top of every file and every __init__.py would not appear on any
# rendered page. Coverage of the things that do get rendered is enforced by interrogate instead.
"D100",
"D104",
]
[tool.ruff.lint.per-file-ignores]
"__init__.py" = ["F401", "F403", "E402"]
"__init__.py" = ["F401", "F403", "E402", "D104"]
# E402: conditional-import guards (TYPE_CHECKING / is_package_available) must precede the imports they protect
"src/lerobot/scripts/convert_dataset_v21_to_v30.py" = ["E402"]
# D (pydocstyle) is enabled globally, but only holds for code that has been converted to the docstring
# standard in docs/source/writing_docstrings.mdx. Every module below is still on the old style; each entry
# is deleted as that module is converted, and this block can be removed once it is empty.
#
# Not part of the API reference and not planned for conversion: tests, examples, benchmarks, templates,
# CI helper scripts and the packaging shim.
"tests/**" = ["D"]
"examples/**" = ["D"]
"benchmarks/**" = ["D"]
"scripts/**" = ["D"]
"setup.py" = ["D"]
"src/lerobot/templates/**" = ["D"]
# Vendored from transformers; keeps its upstream docstring style so syncs stay clean.
"src/lerobot/policies/molmoact2/molmoact2_hf_model/**" = ["D"]
# Awaiting conversion, one PR per module.
"src/lerobot/annotations/**" = ["D"]
"src/lerobot/async_inference/**" = ["D"]
"src/lerobot/cameras/**" = ["D"]
"src/lerobot/common/**" = ["D"]
"src/lerobot/configs/**" = ["D"]
"src/lerobot/data_processing/**" = ["D"]
"src/lerobot/datasets/**" = ["D"]
"src/lerobot/distributed/**" = ["D"]
"src/lerobot/envs/**" = ["D"]
"src/lerobot/jobs/**" = ["D"]
"src/lerobot/model/**" = ["D"]
"src/lerobot/motors/motors_bus.py" = ["D"]
"src/lerobot/optim/**" = ["D"]
"src/lerobot/policies/**" = ["D"]
"src/lerobot/processor/**" = ["D"]
"src/lerobot/rewards/**" = ["D"]
"src/lerobot/rl/**" = ["D"]
"src/lerobot/rollout/**" = ["D"]
"src/lerobot/scripts/**" = ["D"]
"src/lerobot/teleoperators/**" = ["D"]
"src/lerobot/transforms/**" = ["D"]
"src/lerobot/transport/**" = ["D"]
"src/lerobot/utils/**" = ["D"]
"src/lerobot/lerobot_types.py" = ["D"]
[tool.ruff.lint.isort]
combine-as-imports = true
known-first-party = ["lerobot"]
@@ -457,21 +502,24 @@ default.extend-ignore-identifiers-re = [
"seperated_timestep",
]
# TODO: Uncomment when ready to use
# [tool.interrogate]
# ignore-init-module = true
# ignore-init-method = true
# ignore-nested-functions = false
# ignore-magic = false
# ignore-semiprivate = false
# ignore-private = false
# ignore-property-decorators = false
# ignore-module = false
# ignore-setters = false
# fail-under = 80
# output-format = "term-missing"
# color = true
# paths = ["src/lerobot"]
# Docstring coverage gate. `fail-under` is a RATCHET, not a target: it is set just below the currently
# measured coverage so it passes today, and is raised in the same PR that documents a module. Never set it
# to a value that fails on main. The destination is 100; see docs/source/writing_docstrings.mdx.
[tool.interrogate]
ignore-init-module = true
ignore-init-method = true
ignore-nested-functions = false
ignore-magic = false
ignore-semiprivate = false
ignore-private = false
ignore-property-decorators = false
ignore-module = false
ignore-setters = false
fail-under = 55
output-format = "term-missing"
color = true
paths = ["src/lerobot"]
exclude = ["src/lerobot/policies/molmoact2/molmoact2_hf_model"]
# TODO: Enable mypy gradually module by module across multiple PRs
# Uncomment [tool.mypy] first, then uncomment individual module overrides as they get proper type annotations
+1 -2
View File
@@ -14,8 +14,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
LeRobot -- PyTorch library for real-world robotics.
"""LeRobot -- PyTorch library for real-world robotics.
Provides datasets, pretrained policies, and tools for training, evaluation,
data collection, and robot control. Integrates with Hugging Face Hub for
+1 -1
View File
@@ -13,7 +13,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""To enable `lerobot.__version__`"""
"""To enable `lerobot.__version__`."""
from importlib.metadata import PackageNotFoundError, version
+4 -4
View File
@@ -33,10 +33,10 @@ class Camera(abc.ABC):
- Connection/disconnection
- Frame capture (sync/async/latest)
Attributes:
fps (int | None): Configured frames per second
width (int | None): Frame width in pixels
height (int | None): Frame height in pixels
**Attributes**:
- **fps** (`int | None`) -- Configured frames per second.
- **width** (`int | None`) -- Frame width in pixels.
- **height** (`int | None`) -- Frame height in pixels.
"""
def __init__(self, config: CameraConfig):
@@ -40,17 +40,20 @@ class OpenCVCameraConfig(CameraConfig):
OpenCVCameraConfig(0, 30, 1280, 720, fourcc="YUYV") # With YUYV format
```
Attributes:
index_or_path: Either an integer representing the camera device index,
or a Path object pointing to a video file.
fps: Requested frames per second for the color stream.
width: Requested frame width in pixels for the color stream.
height: Requested frame height in pixels for the color stream.
color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
warmup_s: Time reading frames before returning from connect (in seconds)
fourcc: FOURCC code for video format (e.g., "MJPG", "YUYV", "I420"). Defaults to None (auto-detect).
backend: OpenCV backend identifier (https://docs.opencv.org/3.4/d4/d15/group__videoio__flags__base.html). Defaults to ANY.
**Attributes**:
- **index_or_path** (`int | Path`) -- Either an integer representing the camera device index, or a
Path object pointing to a video file.
- **fps** -- Requested frames per second for the color stream.
- **width** -- Requested frame width in pixels for the color stream.
- **height** -- Requested frame height in pixels for the color stream.
- **color_mode** (`ColorMode`) -- Color mode for image output (RGB or BGR). Defaults to RGB.
- **rotation** (`Cv2Rotation`) -- Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no
rotation.
- **warmup_s** (`int`) -- Time reading frames before returning from connect (in seconds)
- **fourcc** (`str | None`) -- FOURCC code for video format (e.g., "MJPG", "YUYV", "I420"). Defaults
to None (auto-detect).
- **backend** (`Cv2Backends`) -- OpenCV backend identifier
(https://docs.opencv.org/3.4/d4/d15/group__videoio__flags__base.html). Defaults to ANY.
Note:
- Only 3-channel color output (RGB/BGR) is currently supported.
@@ -43,16 +43,16 @@ class Reachy2CameraConfig(CameraConfig):
) # Left teleop camera, 640x480 @ 30FPS
```
Attributes:
name: Name of the camera device. Can be "teleop" or "depth".
image_type: Type of image stream. For "teleop" camera, can be "left" or "right".
For "depth" camera, can be "rgb" or "depth". (depth is not supported yet)
fps: Requested frames per second for the color stream. Not configurable for Reachy 2 cameras.
width: Requested frame width in pixels for the color stream.
height: Requested frame height in pixels for the color stream.
color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
ip_address: IP address of the robot. Defaults to "localhost".
port: Port number for the camera server. Defaults to 50065.
**Attributes**:
- **name** (`str`) -- Name of the camera device. Can be "teleop" or "depth".
- **image_type** (`str`) -- Type of image stream. For "teleop" camera, can be "left" or "right". For
"depth" camera, can be "rgb" or "depth". (depth is not supported yet)
- **fps** -- Requested frames per second for the color stream. Not configurable for Reachy 2 cameras.
- **width** -- Requested frame width in pixels for the color stream.
- **height** -- Requested frame height in pixels for the color stream.
- **color_mode** (`ColorMode`) -- Color mode for image output (RGB or BGR). Defaults to RGB.
- **ip_address** (`str | None`) -- IP address of the robot. Defaults to "localhost".
- **port** (`int`) -- Port number for the camera server. Defaults to 50065.
Note:
- Only 3-channel color output (RGB/BGR) is currently supported.
@@ -36,27 +36,28 @@ class RealSenseCameraConfig(CameraConfig):
RealSenseCameraConfig("0123456789", 30, 640, 480, rotation=Cv2Rotation.ROTATE_90) # With 90° rotation
```
Attributes:
fps: Requested frames per second for the color stream.
width: Requested frame width in pixels for the color stream.
height: Requested frame height in pixels for the color stream.
serial_number_or_name: Unique serial number or human-readable name to identify the camera.
color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
use_rgb: Whether to enable the color stream. Defaults to True.
use_depth: Whether to enable depth stream. Defaults to False.
rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
warmup_s: Time reading frames before returning from connect (in seconds)
exposure: Manual exposure value for the color sensor. When set, auto-exposure is
disabled and this fixed value is used. Valid ranges are camera-model specific
and reported if the value is rejected. Defaults to None (leave unchanged).
gain: Manual gain value for the color sensor. When set, auto-exposure is disabled
and this fixed gain is used, which also freezes exposure at its current value
when no exposure is configured. Valid ranges are camera-model specific and
reported if the value is rejected. Defaults to None (leave unchanged).
white_balance: Manual white balance value for the color sensor. When set, auto
white balance is disabled and this fixed value is used. Valid ranges are
camera-model specific and reported if the value is rejected. Defaults to None
(leave unchanged).
**Attributes**:
- **fps** -- Requested frames per second for the color stream.
- **width** -- Requested frame width in pixels for the color stream.
- **height** -- Requested frame height in pixels for the color stream.
- **serial_number_or_name** (`str`) -- Unique serial number or human-readable name to identify the
camera.
- **color_mode** (`ColorMode`) -- Color mode for image output (RGB or BGR). Defaults to RGB.
- **use_rgb** (`bool`) -- Whether to enable the color stream. Defaults to True.
- **use_depth** (`bool`) -- Whether to enable depth stream. Defaults to False.
- **rotation** (`Cv2Rotation`) -- Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no
rotation.
- **warmup_s** (`int`) -- Time reading frames before returning from connect (in seconds)
- **exposure** (`int | None`) -- Manual exposure value for the color sensor. When set, auto-exposure
is disabled and this fixed value is used. Valid ranges are camera-model specific and reported if the
value is rejected. Defaults to None (leave unchanged).
- **gain** (`int | None`) -- Manual gain value for the color sensor. When set, auto-exposure is
disabled and this fixed gain is used, which also freezes exposure at its current value when no
exposure is configured. Valid ranges are camera-model specific and reported if the value is
rejected. Defaults to None (leave unchanged).
- **white_balance** (`int | None`) -- Manual white balance value for the color sensor. When set, auto
white balance is disabled and this fixed value is used. Valid ranges are camera-model specific and
reported if the value is rejected. Defaults to None (leave unchanged).
Note:
- Either name or serial_number must be specified.
+1 -4
View File
@@ -22,7 +22,7 @@ Import them directly: ``from lerobot.configs.train import TrainPipelineConfig``
"""
from .dataset import DatasetRecordConfig
from .default import DatasetConfig, EMAConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
from .default import DatasetConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
from .policies import PreTrainedConfig
from .recipe import MessageTurn, TrainingRecipe, load_recipe
from .types import (
@@ -31,7 +31,6 @@ from .types import (
PipelineFeatureType,
PolicyFeature,
RTCAttentionSchedule,
TextKind,
)
from .video import (
DEFAULT_DEPTH_UNIT,
@@ -55,11 +54,9 @@ __all__ = [
"PipelineFeatureType",
"PolicyFeature",
"RTCAttentionSchedule",
"TextKind",
# Config classes
"DatasetRecordConfig",
"DatasetConfig",
"EMAConfig",
"EvalConfig",
"JobConfig",
"MessageTurn",
-53
View File
@@ -139,59 +139,6 @@ class EvalConfig:
return min(by_cpu, self.n_episodes, 64)
@dataclass
class EMAConfig:
"""Exponential moving average (EMA) of the policy weights.
Standard practice for diffusion-style policies (Chi et al. 2023, "Diffusion Policy", section V.D):
the reference implementation enables it in every config and evaluates the EMA weights. Off by
default here because it keeps a second full copy of the parameters in memory.
The decay follows the warmup schedule from diffusers' `EMAModel`:
`decay_t = 1 - (1 + t / inv_gamma) ** -power`, clamped to `[min_decay, max_decay]`.
The defaults mirror the reference implementation. Alternatively, set `decay` for a constant
decay at every step, as used by openpi for pi0/pi05 (`ema_decay=0.99`).
"""
enable: bool = False
# Constant decay coefficient (openpi-style, e.g. 0.99 for pi0/pi05). When set, the warmup
# schedule below is bypassed and the shadow uses this decay at every step.
decay: float | None = None
# Number of optimizer steps during which the shadow stays a hard copy of the live weights.
update_after_step: int = 0
# Warmup schedule parameters (see class docstring).
inv_gamma: float = 1.0
power: float = 0.75
min_decay: float = 0.0
max_decay: float = 0.9999
# Evaluate the EMA weights (instead of the live ones) during periodic env eval.
# Offline eval-loss (--eval_steps) always uses the live weights: it runs on every rank
# while the EMA shadow only lives on the main process.
use_for_eval: bool = True
def __post_init__(self) -> None:
if not (0.0 <= self.min_decay <= self.max_decay <= 1.0):
raise ValueError(
"Expected 0 <= ema.min_decay <= ema.max_decay <= 1, got "
f"min_decay={self.min_decay} and max_decay={self.max_decay}."
)
if self.inv_gamma <= 0:
raise ValueError(f"ema.inv_gamma must be positive, got {self.inv_gamma}.")
if self.power <= 0:
raise ValueError(f"ema.power must be positive, got {self.power}.")
if self.update_after_step < 0:
raise ValueError(f"ema.update_after_step must be >= 0, got {self.update_after_step}.")
if self.decay is not None:
if not 0.0 <= self.decay <= 1.0:
raise ValueError(f"ema.decay must be in [0, 1], got {self.decay}.")
# Keep the literals in sync with the field defaults above.
if self.min_decay != 0.0 or self.max_decay != 0.9999:
raise ValueError(
"ema.decay (constant decay) and ema.min_decay/ema.max_decay (schedule clamp) are "
"mutually exclusive: set one or the other."
)
@dataclass
class PeftConfig:
# PEFT offers many fine-tuning methods, layer adapters being the most common and currently also the most
-6
View File
@@ -67,12 +67,6 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC): # type: igno
# Whether the policy employed PEFT for training.
use_peft: bool = False
# Decoding defaults for policies that implement `generate_text`. They live
# in config.json so a text head uses the settings it was trained/evaluated
# with; policy-specific decoding knobs belong on the concrete config.
text_temperature: float = 0.0 # 0.0 = greedy; > 0 enables sampling
text_top_p: float = 1.0
push_to_hub: bool = True # type: ignore[assignment] # TODO: use a different name to avoid override
repo_id: str | None = None
+1 -3
View File
@@ -35,7 +35,7 @@ from lerobot.utils.hub import HubMixin, find_latest_hub_checkpoint
from lerobot.utils.sample_weighting import SampleWeightingConfig
from . import parser
from .default import DatasetConfig, EMAConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
from .default import DatasetConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
from .policies import PreTrainedConfig
from .rewards import RewardModelConfig
@@ -163,8 +163,6 @@ class TrainPipelineConfig(HubMixin):
# FSDP/DDP tuning knobs, compile & activation-checkpointing placeholders.
accelerator: AcceleratorConfig = field(default_factory=AcceleratorConfig)
eval: EvalConfig = field(default_factory=EvalConfig)
# Maintain an EMA shadow of the policy weights during training (see EMAConfig).
ema: EMAConfig = field(default_factory=EMAConfig)
wandb: WandBConfig = field(default_factory=WandBConfig)
peft: PeftConfig | None = None
-7
View File
@@ -31,13 +31,6 @@ class PipelineFeatureType(str, Enum):
OBSERVATION = "OBSERVATION"
class TextKind(str, Enum):
"""Text-generation requests understood by interactive policy hooks."""
SUBTASK = "subtask"
VQA = "vqa"
class NormalizationMode(str, Enum):
MIN_MAX = "MIN_MAX"
MEAN_STD = "MEAN_STD"
+2 -9
View File
@@ -613,15 +613,8 @@ def aggregate_feature_stats(stats_ft_list: list[dict[str, dict]]) -> dict[str, d
for q_key in quantile_keys:
if all(q_key in s for s in stats_ft_list):
quantile_values = np.stack([s[q_key] for s in stats_ft_list])
# Exact global quantiles cannot be recovered from quantile summaries.
# Keep a conservative envelope of the available estimates: min
# for lower quantiles and max for upper quantiles. The resulting
# values are bounds across the inputs, not global quantile estimates.
q_percent = int(q_key[1:])
if q_percent <= 50:
aggregated[q_key] = np.min(quantile_values, axis=0)
else:
aggregated[q_key] = np.max(quantile_values, axis=0)
weighted_quantiles = quantile_values * counts
aggregated[q_key] = weighted_quantiles.sum(axis=0) / total_count
return aggregated
+10 -2
View File
@@ -33,7 +33,11 @@ if TYPE_CHECKING:
from lerobot.policies.pretrained import PreTrainedPolicy
# Env vars through which `accelerate launch --config_file` (or a stray shell) would configure
# accelerate behind the config system's back, making train_config.json lie about what ran.
# accelerate behind the config system's back. Plugin `__post_init__`s read these silently as
# field fallbacks (ACCELERATE_DYNAMO_* enables torch.compile through the default
# TorchDynamoPlugin; ACCELERATE_GRADIENT_ACCUMULATION_STEPS overrides the explicitly passed
# value inside Accelerator.__init__), which would make train_config.json lie about what ran.
_ACCELERATE_ENV_PREFIXES = ("FSDP_", "PARALLELISM_CONFIG_", "ACCELERATE_DYNAMO_")
_ACCELERATE_ENV_VARS = (
"ACCELERATE_USE_FSDP",
"ACCELERATE_USE_PARALLELISM_CONFIG",
@@ -55,7 +59,11 @@ def guard_against_env_interference() -> None:
"""
if os.environ.get(_ENV_OVERRIDE):
return
offending = sorted(name for name in _ACCELERATE_ENV_VARS if name in os.environ)
offending = sorted(
name
for name in os.environ
if name in _ACCELERATE_ENV_VARS or name.startswith(_ACCELERATE_ENV_PREFIXES)
)
if offending:
raise RuntimeError(
f"Accelerate-configuring environment variables are set: {', '.join(offending)}. "
+49 -1
View File
@@ -43,20 +43,43 @@ DD_COLOR, DD_COLOR_HL = (70, 70, 70), (100, 100, 100)
def dist(a, b):
"""Return the Euclidean distance between two `(x, y)` points."""
return math.hypot(a[0] - b[0], a[1] - b[1])
@dataclass
class RangeValues:
"""The min/current/max motor step values read off one `RangeSlider`.
Args:
min_v (`int`):
Lower bound of the calibrated range, in raw motor steps.
pos_v (`int`):
Current position, in raw motor steps.
max_v (`int`):
Upper bound of the calibrated range, in raw motor steps.
"""
min_v: int
pos_v: int
max_v: int
class RangeSlider:
"""One motor = one slider row"""
"""A draggable min/current/max slider row for one motor in the range-finder GUI."""
def __init__(self, motor, idx, res, calibration, present, label_pad, base_y):
"""Lay out one slider row from the motor's current calibration and position.
Args:
motor: Motor name this row controls.
idx: Row index, used to compute its vertical position.
res: Motor resolution (number of steps), used to convert steps to pixels.
calibration: The motor's current `MotorCalibration`, for the initial min/max handles.
present: The motor's current position, in raw steps.
label_pad: Pixel width reserved for the widest motor name label.
base_y: Vertical pixel offset of the first row.
"""
import pygame
self.motor = motor
@@ -87,6 +110,7 @@ class RangeSlider:
return self.x0 + (v / self.res) * BAR_LEN
def set_tick(self, v):
"""Move the live position tick to `v`, clamped to `[0, res]`."""
self.tick_val = max(0, min(v, self.res))
def _triangle_hit(self, pos):
@@ -96,6 +120,7 @@ class RangeSlider:
return pygame.Rect(self.pos_x - TRI_W // 2, tri_top - TRI_H, TRI_W, TRI_H).collidepoint(pos)
def handle_event(self, e):
"""Update the min/max/position handles in response to one pygame event."""
import pygame
if e.type == pygame.MOUSEBUTTONDOWN and e.button == 1:
@@ -137,6 +162,7 @@ class RangeSlider:
surf.blit(t, (rect.centerx - t.get_width() // 2, rect.centery - t.get_height() // 2))
def draw(self, surf):
"""Render this row's bar, handles, labels, and buttons onto `surf`."""
import pygame
# motor name above set-min button (right-aligned)
@@ -212,11 +238,25 @@ class RangeSlider:
# external
def values(self) -> RangeValues:
"""Return this row's current min/current/max values as a `RangeValues`."""
return RangeValues(self.min_v, self.pos_v, self.max_v)
class RangeFinderGUI:
"""A pygame window with one slider per motor for finding calibration ranges interactively.
Drag a motor's handles to set its min/max range, or the triangle to move it to a position. Motors can
be split into named groups (shown in a dropdown) when there are too many to fit one screen.
"""
def __init__(self, bus: MotorsBus, groups: dict[str, list[str]] | None = None):
"""Connect to the bus if needed, read its current calibration, and lay out one slider per motor.
Args:
bus: The connected (or connectable) `MotorsBus` whose motors to calibrate.
groups: Named subsets of `bus.motors` to show one at a time via a dropdown. `None` puts every
motor in a single `"all"` group.
"""
import pygame
self.bus = bus
@@ -354,6 +394,14 @@ class RangeFinderGUI:
s.max_x = s._pos_from_val(s.max_v)
def run(self) -> dict[str, MotorCalibration]:
"""Run the GUI event loop until the window is closed.
Writes live goal positions to the bus while a handle is being dragged, and writes the new
calibration to the bus when "SAVE" is clicked.
Returns:
`dict[str, MotorCalibration]`: The calibration in effect when the window was closed.
"""
import pygame
while True:
+36 -41
View File
@@ -30,6 +30,8 @@ if TYPE_CHECKING or _can_available:
else:
class can: # noqa: N801
"""Fallback stand-in for the `python-can` package when it is not installed."""
Message = object
interface = None
@@ -65,6 +67,16 @@ PRECISE_TIMEOUT_SEC = 0.0001
class MotorState(TypedDict):
"""A Damiao motor's last-reported feedback frame.
**Attributes**:
- **position** (`float`) -- Position, in radians.
- **velocity** (`float`) -- Velocity, in radians per second.
- **torque** (`float`) -- Torque, in newton-meters.
- **temp_mos** (`float`) -- MOSFET temperature, in degrees Celsius.
- **temp_rotor** (`float`) -- Rotor temperature, in degrees Celsius.
"""
position: float
velocity: float
torque: float
@@ -73,8 +85,7 @@ class MotorState(TypedDict):
class DamiaoMotorsBus(MotorsBusBase):
"""
The Damiao implementation for a MotorsBus using CAN bus communication.
"""The Damiao implementation for a MotorsBus using CAN bus communication.
This class uses python-can for CAN bus communication with Damiao motors.
For more info, see:
@@ -98,8 +109,7 @@ class DamiaoMotorsBus(MotorsBusBase):
bitrate: int = 1000000,
data_bitrate: int | None = 5000000,
):
"""
Initialize the Damiao motors bus.
"""Initialize the Damiao motors bus.
Args:
port: CAN interface name (e.g., "can0" for Linux, "/dev/cu.usbmodem*" for macOS)
@@ -157,13 +167,11 @@ class DamiaoMotorsBus(MotorsBusBase):
@check_if_already_connected
def connect(self, handshake: bool = True) -> None:
"""
Open the CAN bus and initialize communication.
"""Open the CAN bus and initialize communication.
Args:
handshake: If True, ping all motors to verify they're present
"""
try:
# Auto-detect interface type based on port name
if self.can_interface == "auto":
@@ -201,8 +209,8 @@ class DamiaoMotorsBus(MotorsBusBase):
raise ConnectionError(f"Failed to connect to CAN bus: {e}") from e
def _handshake(self) -> None:
"""
Verify all motors are present and populate initial state cache.
"""Verify all motors are present and populate initial state cache.
Raises ConnectionError if any motor fails to respond.
"""
logger.info("Starting handshake with motors...")
@@ -248,13 +256,11 @@ class DamiaoMotorsBus(MotorsBusBase):
@check_if_not_connected
def disconnect(self, disable_torque: bool = True) -> None:
"""
Close the CAN bus connection.
"""Close the CAN bus connection.
Args:
disable_torque: If True, disable torque on all motors before disconnecting
"""
if disable_torque:
try:
self.disable_torque()
@@ -320,8 +326,7 @@ class DamiaoMotorsBus(MotorsBusBase):
@contextmanager
def torque_disabled(self, motors: str | list[str] | None = None):
"""
Context manager that guarantees torque is re-enabled.
"""Context manager that guarantees torque is re-enabled.
This helper is useful to temporarily disable torque when configuring motors.
"""
@@ -354,16 +359,15 @@ class DamiaoMotorsBus(MotorsBusBase):
def _recv_motor_response(
self, expected_recv_id: int | None = None, timeout: float = 0.001
) -> can.Message | None:
"""
Receive a response from a motor.
"""Receive a response from a motor.
Args:
expected_recv_id: If provided, only return messages from this CAN ID
timeout: Timeout in seconds (default: 1ms for high-speed operation)
Returns:
CAN message if received, None otherwise
"""
if self.canbus is None:
raise RuntimeError("CAN bus is not initialized.")
@@ -394,8 +398,8 @@ class DamiaoMotorsBus(MotorsBusBase):
def _recv_all_responses(
self, expected_recv_ids: list[int], timeout: float = 0.002
) -> dict[int, can.Message]:
"""
Efficiently receive responses from multiple motors at once.
"""Efficiently receive responses from multiple motors at once.
Uses the OpenArms pattern: collect all available messages within timeout.
Args:
@@ -492,8 +496,8 @@ class DamiaoMotorsBus(MotorsBusBase):
self,
commands: dict[NameOrID, tuple[float, float, float, float, float]],
) -> None:
"""
Send MIT control commands to multiple motors in batch.
"""Send MIT control commands to multiple motors in batch.
Sends all commands first, then collects responses.
Args:
@@ -542,9 +546,10 @@ class DamiaoMotorsBus(MotorsBusBase):
def _decode_motor_state(
self, data: bytearray | bytes, motor_type: MotorType
) -> tuple[float, float, float, int, int]:
"""
Decode motor state from CAN data.
Returns: (position_deg, velocity_deg_s, torque, temp_mos, temp_rotor)
"""Decode motor state from CAN data.
Returns:
`tuple[float, float, float, int, int]`: `(position_deg, velocity_deg_s, torque, temp_mos, temp_rotor)`.
"""
if len(data) < 8:
raise ValueError("Invalid motor state data")
@@ -585,7 +590,6 @@ class DamiaoMotorsBus(MotorsBusBase):
@check_if_not_connected
def read(self, data_name: str, motor: str) -> Value:
"""Read a value from a single motor. Positions are always in degrees."""
# Refresh motor to get latest state
msg = self._refresh_motor(motor)
if msg is None:
@@ -621,11 +625,10 @@ class DamiaoMotorsBus(MotorsBusBase):
motor: str,
value: Value,
) -> None:
"""
Write a value to a single motor. Positions are always in degrees.
Can write 'Goal_Position', 'Kp', or 'Kd'.
"""
"""Write a value to a single motor. Positions are always in degrees.
Can write `'Goal_Position'`, `'Kp'`, or `'Kd'`.
"""
if data_name in ("Kp", "Kd"):
self._gains[motor][data_name.lower()] = float(value)
elif data_name == "Goal_Position":
@@ -640,9 +643,7 @@ class DamiaoMotorsBus(MotorsBusBase):
data_name: str,
motors: str | list[str] | None = None,
) -> dict[str, Value]:
"""
Read the same value from multiple motors simultaneously.
"""
"""Read the same value from multiple motors simultaneously."""
target_motors = self._get_motors_list(motors)
self._batch_refresh(target_motors)
@@ -657,8 +658,7 @@ class DamiaoMotorsBus(MotorsBusBase):
*,
num_retry: int = 0,
) -> dict[str, MotorState]:
"""
Read ALL motor states (position, velocity, torque) from multiple motors in ONE refresh cycle.
"""Read ALL motor states (position, velocity, torque) from multiple motors in ONE refresh cycle.
Returns:
Dictionary mapping motor names to state dicts with keys: 'position', 'velocity', 'torque'
@@ -674,7 +674,6 @@ class DamiaoMotorsBus(MotorsBusBase):
def _batch_refresh(self, motors: list[str]) -> None:
"""Internal helper to refresh a list of motors and update cache."""
if self.canbus is None:
raise RuntimeError("CAN bus is not initialized.")
@@ -702,10 +701,7 @@ class DamiaoMotorsBus(MotorsBusBase):
@check_if_not_connected
def sync_write(self, data_name: str, values: dict[str, Value]) -> None:
"""
Write values to multiple motors simultaneously. Positions are always in degrees.
"""
"""Write values to multiple motors simultaneously. Positions are always in degrees."""
if data_name in ("Kp", "Kd"):
key = data_name.lower()
for motor, val in values.items():
@@ -761,8 +757,7 @@ class DamiaoMotorsBus(MotorsBusBase):
motors: str | list[str] | None = None,
display_values: bool = True,
) -> tuple[dict[str, Value], dict[str, Value]]:
"""
Interactively record the min/max values of each motor in degrees.
"""Interactively record the min/max values of each motor in degrees.
Move the joints by hand (with torque disabled) while the method streams live positions.
Press Enter to finish.
+18
View File
@@ -19,6 +19,8 @@ from enum import IntEnum
# Motor type definitions
class MotorType(IntEnum):
"""The Damiao motor model, selecting its limit and gearing parameters."""
DM3507 = 0
DM4310 = 1
DM4310_48V = 2
@@ -36,6 +38,16 @@ class MotorType(IntEnum):
# Control modes
class ControlMode(IntEnum):
"""The Damiao motor's active control mode.
**Attributes**:
- **MIT** -- Combined position/velocity/torque control over CAN, following the MIT Cheetah
protocol.
- **POS_VEL** -- Position and velocity control.
- **VEL** -- Velocity-only control.
- **TORQUE_POS** -- Torque and position control.
"""
MIT = 1
POS_VEL = 2
VEL = 3
@@ -44,6 +56,12 @@ class ControlMode(IntEnum):
# Motor variable IDs (RID)
class MotorVariable(IntEnum):
"""Register IDs (RID) for a Damiao motor's internal parameter table.
Used with the motor's read/write-parameter CAN commands to get or set a single tunable, e.g. current
limits, PID gains, or CAN bitrate. See Damiao's motor manual for the meaning of each register.
"""
UV_VALUE = 0
KT_VALUE = 1
OT_VALUE = 2
+102 -7
View File
@@ -51,6 +51,24 @@ logger = logging.getLogger(__name__)
class OperatingMode(Enum):
"""Control mode written to a Dynamixel motor's `Operating_Mode` register.
**Attributes**:
- **CURRENT** -- Torque-only control, ideal for a gripper or a system with its own
velocity/position controllers.
- **VELOCITY** -- Velocity control, identical to the Wheel Mode (endless) from existing Dynamixel.
Ideal for wheel-type robots.
- **POSITION** -- Position control, identical to the Joint Mode from existing Dynamixel. Range is
limited by the Max/Min Position Limit. Ideal for articulated robots whose joints rotate less
than 360 degrees.
- **EXTENDED_POSITION** -- Multi-turn position control, supporting up to 512 turns (-256 to 256
revolutions). The Max/Min Position Limit is not used in this mode. Ideal for multi-turn wrists,
conveyor systems, or a system with an additional reduction gear.
- **CURRENT_POSITION** -- Combined position and torque control, up to 512 turns. Ideal for a
system that needs both, such as articulated robots or grippers.
- **PWM** -- Direct PWM (voltage) control.
"""
# DYNAMIXEL only controls current(torque) regardless of speed and position. This mode is ideal for a
# gripper or a system that only uses current(torque) control or a system that has additional
# velocity/position controllers.
@@ -81,20 +99,34 @@ class OperatingMode(Enum):
class DriveMode(Enum):
"""Whether a Dynamixel motor's rotation direction is inverted.
**Attributes**:
- **NON_INVERTED** -- Positive commands rotate the motor in its default direction.
- **INVERTED** -- Positive commands rotate the motor in the opposite direction.
"""
NON_INVERTED = 0
INVERTED = 1
class TorqueMode(Enum):
"""Whether a Dynamixel motor's torque is enabled.
**Attributes**:
- **ENABLED** -- The motor holds position/velocity and resists external force.
- **DISABLED** -- The motor is free to move by hand.
"""
ENABLED = 1
DISABLED = 0
class DynamixelMotorsBus(SerialMotorsBus):
"""
The Dynamixel implementation for a MotorsBus. It relies on the python dynamixel sdk to communicate with
the motors. For more info, see the Dynamixel SDK Documentation:
https://emanual.robotis.com/docs/en/software/dynamixel/dynamixel_sdk/sample_code/python_read_write_protocol_2_0/#python-read-write-protocol-20
"""The Dynamixel implementation of [`~motors.motors_bus.MotorsBus`].
Relies on the [Dynamixel SDK](https://emanual.robotis.com/docs/en/software/dynamixel/dynamixel_sdk/sample_code/python_read_write_protocol_2_0/#python-read-write-protocol-20)
to talk to the motors over a serial port. Protocol 2.0 only.
"""
apply_drive_mode = False
@@ -114,6 +146,17 @@ class DynamixelMotorsBus(SerialMotorsBus):
motors: dict[str, Motor],
calibration: dict[str, MotorCalibration] | None = None,
):
"""Set up the bus without opening the port; call [`~motors.motors_bus.MotorsBus.connect`] to talk to it.
Args:
port (`str`):
Serial port the motors are connected to, e.g. `/dev/ttyACM0`.
motors (`dict[str, Motor]`):
Motors on this bus, keyed by name, e.g. `{"shoulder_pan": Motor(id=1, model="xl430-w250")}`.
calibration (`dict[str, MotorCalibration]`, *optional*):
Cached calibration to use instead of reading it from the motors on connect. `None` reads
it from the motors instead.
"""
require_package("dynamixel-sdk", extra="dynamixel", import_name="dynamixel_sdk")
super().__init__(port, motors, calibration)
self.port_handler = dxl.PortHandler(self.port)
@@ -152,6 +195,12 @@ class DynamixelMotorsBus(SerialMotorsBus):
raise RuntimeError(f"Motor '{motor}' (model '{model}') was not found. Make sure it is connected.")
def configure_motors(self, return_delay_time=0) -> None:
"""Reduce every motor's `Return_Delay_Time` from its 500µs factory default.
Args:
return_delay_time (`int`, *optional*, defaults to 0):
Value written to `Return_Delay_Time`, in units of 2µs.
"""
# By default, Dynamixel motors have a 500µs delay response time (corresponding to a value of 250 on
# the 'Return_Delay_Time' address). We ensure this is reduced to the minimum of 2µs (value of 0).
for motor in self.motors:
@@ -159,9 +208,15 @@ class DynamixelMotorsBus(SerialMotorsBus):
@property
def is_calibrated(self) -> bool:
"""`bool`: `True` if the cached calibration matches what [`~motors.dynamixel.DynamixelMotorsBus.read_calibration`] returns."""
return self.calibration == self.read_calibration()
def read_calibration(self) -> dict[str, MotorCalibration]:
"""Read each motor's homing offset and position limits from its `Homing_Offset`, `Min_Position_Limit`, `Max_Position_Limit`, and `Drive_Mode` registers.
Returns:
`dict[str, MotorCalibration]`: Calibration keyed by motor name.
"""
offsets = self.sync_read("Homing_Offset", normalize=False)
mins = self.sync_read("Min_Position_Limit", normalize=False)
maxes = self.sync_read("Max_Position_Limit", normalize=False)
@@ -180,6 +235,14 @@ class DynamixelMotorsBus(SerialMotorsBus):
return calibration
def write_calibration(self, calibration_dict: dict[str, MotorCalibration], cache: bool = True) -> None:
"""Write each motor's homing offset and position limits to the bus.
Args:
calibration_dict (`dict[str, MotorCalibration]`):
Calibration to write, keyed by motor name.
cache (`bool`, *optional*, defaults to `True`):
Whether to also store `calibration_dict` as `self.calibration`.
"""
for motor, calibration in calibration_dict.items():
self.write("Homing_Offset", motor, calibration.homing_offset)
self.write("Min_Position_Limit", motor, calibration.range_min)
@@ -189,6 +252,15 @@ class DynamixelMotorsBus(SerialMotorsBus):
self.calibration = calibration_dict
def disable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
"""Same as [`~motors.motors_bus.MotorsBus.disable_torque`].
Args:
motors (`int | str | list[str]`, *optional*):
Target motors. Accepts a motor name, an ID, a list of names, or `None` for every
registered motor.
num_retry (`int`, *optional*, defaults to 0):
Number of additional retry attempts on communication failure.
"""
for motor in self._get_motors_list(motors):
self.write("Torque_Enable", motor, TorqueMode.DISABLED.value, num_retry=num_retry)
@@ -197,6 +269,15 @@ class DynamixelMotorsBus(SerialMotorsBus):
self._write(addr, length, motor, TorqueMode.DISABLED.value, num_retry=num_retry)
def enable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
"""Same as [`~motors.motors_bus.MotorsBus.enable_torque`].
Args:
motors (`int | str | list[str]`, *optional*):
Target motors. Accepts a motor name, an ID, a list of names, or `None` for every
registered motor.
num_retry (`int`, *optional*, defaults to 0):
Number of additional retry attempts on communication failure.
"""
for motor in self._get_motors_list(motors):
self.write("Torque_Enable", motor, TorqueMode.ENABLED.value, num_retry=num_retry)
@@ -221,9 +302,9 @@ class DynamixelMotorsBus(SerialMotorsBus):
return ids_values
def _get_half_turn_homings(self, positions: dict[NameOrID, Value]) -> dict[NameOrID, Value]:
"""
On Dynamixel Motors:
Present_Position = Actual_Position + Homing_Offset
"""Compute the homing offset that centers `positions` at half a turn.
On Dynamixel motors, `Present_Position = Actual_Position + Homing_Offset`.
"""
half_turn_homings: dict[NameOrID, Value] = {}
for motor, pos in positions.items():
@@ -248,6 +329,20 @@ class DynamixelMotorsBus(SerialMotorsBus):
return data
def broadcast_ping(self, num_retry: int = 0, raise_on_error: bool = False) -> dict[int, int] | None:
"""Same as [`~motors.motors_bus.MotorsBus.broadcast_ping`].
Args:
num_retry (`int`, *optional*, defaults to 0):
Number of additional retry attempts on communication failure.
raise_on_error (`bool`, *optional*, defaults to `False`):
Whether a failure raises `ConnectionError` instead of returning `None`.
Returns:
`dict[int, int] | None`: Mapping of found motor ID to model number, or `None` on failure.
Raises:
ConnectionError: If `raise_on_error` is `True` and the ping fails.
"""
for n_try in range(1 + num_retry):
data_list, comm = self.packet_handler.broadcastPing(self.port_handler)
if self._is_comm_success(comm):
+78 -9
View File
@@ -14,8 +14,26 @@
def encode_sign_magnitude(value: int, sign_bit_index: int):
"""
https://en.wikipedia.org/wiki/Signed_number_representations#Sign%E2%80%93magnitude
"""Encode a signed integer using [sign-magnitude representation](https://en.wikipedia.org/wiki/Signed_number_representations#Sign%E2%80%93magnitude).
Args:
value (`int`):
The signed value to encode.
sign_bit_index (`int`):
Index of the bit that carries the sign; bits below it carry the magnitude.
Returns:
`int`: The encoded value, with the sign bit set for negative inputs.
Raises:
ValueError: If `value`'s magnitude does not fit in `sign_bit_index` bits.
Example:
```python
>>> from lerobot.motors.encoding_utils import encode_sign_magnitude
>>> encode_sign_magnitude(-5, sign_bit_index=7)
133
```
"""
max_magnitude = (1 << sign_bit_index) - 1
magnitude = abs(value)
@@ -27,8 +45,25 @@ def encode_sign_magnitude(value: int, sign_bit_index: int):
def decode_sign_magnitude(encoded_value: int, sign_bit_index: int):
"""
https://en.wikipedia.org/wiki/Signed_number_representations#Sign%E2%80%93magnitude
"""Decode a [sign-magnitude](https://en.wikipedia.org/wiki/Signed_number_representations#Sign%E2%80%93magnitude)-encoded integer.
The inverse of [`~motors.encoding_utils.encode_sign_magnitude`].
Args:
encoded_value (`int`):
The encoded value to decode.
sign_bit_index (`int`):
Index of the bit that carries the sign; bits below it carry the magnitude.
Returns:
`int`: The decoded signed value.
Example:
```python
>>> from lerobot.motors.encoding_utils import decode_sign_magnitude
>>> decode_sign_magnitude(133, sign_bit_index=7)
-5
```
"""
direction_bit = (encoded_value >> sign_bit_index) & 1
magnitude_mask = (1 << sign_bit_index) - 1
@@ -37,10 +72,27 @@ def decode_sign_magnitude(encoded_value: int, sign_bit_index: int):
def encode_twos_complement(value: int, n_bytes: int):
"""
https://en.wikipedia.org/wiki/Signed_number_representations#Two%27s_complement
"""
"""Encode a signed integer using [two's-complement representation](https://en.wikipedia.org/wiki/Signed_number_representations#Two%27s_complement).
Args:
value (`int`):
The signed value to encode.
n_bytes (`int`):
Width of the encoding, in bytes.
Returns:
`int`: The encoded value.
Raises:
ValueError: If `value` does not fit in `n_bytes` bytes.
Example:
```python
>>> from lerobot.motors.encoding_utils import encode_twos_complement
>>> encode_twos_complement(-5, n_bytes=1)
251
```
"""
bit_width = n_bytes * 8
min_val = -(1 << (bit_width - 1))
max_val = (1 << (bit_width - 1)) - 1
@@ -57,8 +109,25 @@ def encode_twos_complement(value: int, n_bytes: int):
def decode_twos_complement(value: int, n_bytes: int) -> int:
"""
https://en.wikipedia.org/wiki/Signed_number_representations#Two%27s_complement
"""Decode a [two's-complement](https://en.wikipedia.org/wiki/Signed_number_representations#Two%27s_complement)-encoded integer.
The inverse of [`~motors.encoding_utils.encode_twos_complement`].
Args:
value (`int`):
The encoded value to decode.
n_bytes (`int`):
Width of the encoding, in bytes.
Returns:
`int`: The decoded signed value.
Example:
```python
>>> from lerobot.motors.encoding_utils import decode_twos_complement
>>> decode_twos_complement(251, n_bytes=1)
-5
```
"""
bits = n_bytes * 8
sign_bit = 1 << (bits - 1)
+112 -8
View File
@@ -50,6 +50,16 @@ logger = logging.getLogger(__name__)
class OperatingMode(Enum):
"""Control mode written to a Feetech motor's `Operating_Mode` register.
**Attributes**:
- **POSITION** -- Position servo mode.
- **VELOCITY** -- Constant speed mode, controlled by parameter `0x2e`; bit 15 is the direction bit.
- **PWM** -- PWM open-loop speed regulation mode, controlled by parameter `0x2c`; bit 11 is the
direction bit.
- **STEP** -- Step servo mode, with step progress in parameter `0x2a`; bit 15 is the direction bit.
"""
# position servo mode
POSITION = 0
# The motor is in constant speed mode, which is controlled by parameter 0x2e, and the highest bit 15 is
@@ -64,18 +74,31 @@ class OperatingMode(Enum):
class DriveMode(Enum):
"""Whether a Feetech motor's rotation direction is inverted.
**Attributes**:
- **NON_INVERTED** -- Positive commands rotate the motor in its default direction.
- **INVERTED** -- Positive commands rotate the motor in the opposite direction.
"""
NON_INVERTED = 0
INVERTED = 1
class TorqueMode(Enum):
"""Whether a Feetech motor's torque is enabled.
**Attributes**:
- **ENABLED** -- The motor holds position/velocity and resists external force.
- **DISABLED** -- The motor is free to move by hand.
"""
ENABLED = 1
DISABLED = 0
def patch_setPacketTimeout(self, packet_length): # noqa: N802
"""
HACK: This patches the PortHandler behavior to set the correct packet timeouts.
"""HACK: This patches the PortHandler behavior to set the correct packet timeouts.
It fixes https://gitee.com/ftservo/SCServoSDK/issues/IBY2S6
The bug is fixed on the official Feetech SDK repo (https://gitee.com/ftservo/FTServo_Python)
@@ -87,9 +110,10 @@ def patch_setPacketTimeout(self, packet_length): # noqa: N802
class FeetechMotorsBus(SerialMotorsBus):
"""
The FeetechMotorsBus class allows to efficiently read and write to the attached motors. It relies on the
python feetech sdk to communicate with the motors, which is itself based on the dynamixel sdk.
"""The Feetech implementation of [`~motors.motors_bus.MotorsBus`].
Relies on the Feetech servo SDK (itself based on the Dynamixel SDK) to talk to the motors over a
serial port. Supports both Feetech protocol 0 and 1, selected via `protocol_version`.
"""
apply_drive_mode = True
@@ -110,6 +134,22 @@ class FeetechMotorsBus(SerialMotorsBus):
calibration: dict[str, MotorCalibration] | None = None,
protocol_version: int = DEFAULT_PROTOCOL_VERSION,
):
"""Set up the bus without opening the port; call [`~motors.motors_bus.MotorsBus.connect`] to talk to it.
Args:
port (`str`):
Serial port the motors are connected to, e.g. `/dev/ttyACM0`.
motors (`dict[str, Motor]`):
Motors on this bus, keyed by name, e.g. `{"gripper": Motor(id=1, model="sts3215")}`.
calibration (`dict[str, MotorCalibration]`, *optional*):
Cached calibration to use instead of reading it from the motors on connect. `None` reads
it from the motors instead.
protocol_version (`int`, *optional*, defaults to 0):
Feetech protocol version. All configured motors must use the same one.
Raises:
ValueError: If a motor's model is incompatible with `protocol_version`.
"""
require_package("feetech-servo-sdk", extra="feetech", import_name="scservo_sdk")
super().__init__(port, motors, calibration)
self.protocol_version = protocol_version
@@ -207,6 +247,19 @@ class FeetechMotorsBus(SerialMotorsBus):
raise RuntimeError(f"Motor '{motor}' (model '{model}') was not found. Make sure it is connected.")
def configure_motors(self, return_delay_time=0, maximum_acceleration=254, acceleration=254) -> None:
"""Reduce every motor's `Return_Delay_Time` and raise its acceleration limits from factory defaults.
Also clears the STS3215's angle-feedback bit, which otherwise lets position readings overflow or
go negative.
Args:
return_delay_time (`int`, *optional*, defaults to 0):
Value written to `Return_Delay_Time`, in units of 2µs.
maximum_acceleration (`int`, *optional*, defaults to 254):
Value written to `Maximum_Acceleration`. Only applies to protocol 0.
acceleration (`int`, *optional*, defaults to 254):
Value written to `Acceleration`.
"""
for motor in self.motors:
# By default, Feetech motors have a 500µs delay response time (corresponding to a value of 250 on
# the 'Return_Delay_Time' address). We ensure this is reduced to the minimum of 2µs (value of 0).
@@ -226,6 +279,10 @@ class FeetechMotorsBus(SerialMotorsBus):
@property
def is_calibrated(self) -> bool:
"""`bool`: `True` if the cached calibration matches what [`~motors.feetech.FeetechMotorsBus.read_calibration`] returns.
Under protocol 1, only the position range is compared protocol 1 motors have no homing offset.
"""
motors_calibration = self.read_calibration()
if set(motors_calibration) != set(self.calibration):
return False
@@ -245,6 +302,12 @@ class FeetechMotorsBus(SerialMotorsBus):
return same_ranges and same_offsets
def read_calibration(self) -> dict[str, MotorCalibration]:
"""Read each motor's position limits, and homing offset under protocol 0, from the bus.
Returns:
`dict[str, MotorCalibration]`: Calibration keyed by motor name. `homing_offset` is always `0`
under protocol 1, which has no such register.
"""
offsets, mins, maxes = {}, {}, {}
for motor in self.motors:
mins[motor] = self.read("Min_Position_Limit", motor, normalize=False)
@@ -266,6 +329,14 @@ class FeetechMotorsBus(SerialMotorsBus):
return calibration
def write_calibration(self, calibration_dict: dict[str, MotorCalibration], cache: bool = True) -> None:
"""Write each motor's position limits, and homing offset under protocol 0, to the bus.
Args:
calibration_dict (`dict[str, MotorCalibration]`):
Calibration to write, keyed by motor name.
cache (`bool`, *optional*, defaults to `True`):
Whether to also store `calibration_dict` as `self.calibration`.
"""
for motor, calibration in calibration_dict.items():
if self.protocol_version == 0:
self.write("Homing_Offset", motor, calibration.homing_offset)
@@ -276,9 +347,9 @@ class FeetechMotorsBus(SerialMotorsBus):
self.calibration = calibration_dict
def _get_half_turn_homings(self, positions: dict[NameOrID, Value]) -> dict[NameOrID, Value]:
"""
On Feetech Motors:
Present_Position = Actual_Position - Homing_Offset
"""Compute the homing offset that centers `positions` at half a turn.
On Feetech motors, `Present_Position = Actual_Position - Homing_Offset`.
"""
half_turn_homings: dict[NameOrID, Value] = {}
for motor, pos in positions.items():
@@ -289,6 +360,15 @@ class FeetechMotorsBus(SerialMotorsBus):
return half_turn_homings
def disable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
"""Same as [`~motors.motors_bus.MotorsBus.disable_torque`]; also clears the `Lock` register so EPROM writes take effect.
Args:
motors (`int | str | list[str]`, *optional*):
Target motors. Accepts a motor name, an ID, a list of names, or `None` for every
registered motor.
num_retry (`int`, *optional*, defaults to 0):
Number of additional retry attempts on communication failure.
"""
for motor in self._get_motors_list(motors):
self.write("Torque_Enable", motor, TorqueMode.DISABLED.value, num_retry=num_retry)
self.write("Lock", motor, 0, num_retry=num_retry)
@@ -300,6 +380,15 @@ class FeetechMotorsBus(SerialMotorsBus):
self._write(addr, length, motor, 0, num_retry=num_retry)
def enable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
"""Same as [`~motors.motors_bus.MotorsBus.enable_torque`]; also sets the `Lock` register to protect EPROM values.
Args:
motors (`int | str | list[str]`, *optional*):
Target motors. Accepts a motor name, an ID, a list of names, or `None` for every
registered motor.
num_retry (`int`, *optional*, defaults to 0):
Number of additional retry attempts on communication failure.
"""
for motor in self._get_motors_list(motors):
self.write("Torque_Enable", motor, TorqueMode.ENABLED.value, num_retry=num_retry)
self.write("Lock", motor, 1, num_retry=num_retry)
@@ -408,6 +497,21 @@ class FeetechMotorsBus(SerialMotorsBus):
rx_length = rx_length - idx
def broadcast_ping(self, num_retry: int = 0, raise_on_error: bool = False) -> dict[int, int] | None:
"""Same as [`~motors.motors_bus.MotorsBus.broadcast_ping`]. Only available under protocol 0.
Args:
num_retry (`int`, *optional*, defaults to 0):
Number of additional retry attempts on communication failure.
raise_on_error (`bool`, *optional*, defaults to `False`):
Whether a failure raises `ConnectionError` instead of returning `None`.
Returns:
`dict[int, int] | None`: Mapping of found motor ID to model number, or `None` on failure.
Raises:
NotImplementedError: If the bus is using protocol 1, which has no broadcast ping instruction.
ConnectionError: If `raise_on_error` is `True` and the ping fails.
"""
self._assert_protocol_is_compatible("broadcast_ping")
for n_try in range(1 + num_retry):
ids_status, comm = self._broadcast_ping()
+24 -17
View File
@@ -314,11 +314,16 @@ class SerialMotorsBus(MotorsBusBase):
To find the port, you can run our utility script:
```bash
lerobot-find-port.py
>>> Finding all available ports for the MotorsBus.
>>> ["/dev/tty.usbmodem575E0032081", "/dev/tty.usbmodem575E0031751"]
>>> Remove the usb cable from your MotorsBus and press Enter when done.
>>> The port of this MotorsBus is /dev/tty.usbmodem575E0031751.
>>> Reconnect the usb cable.
```
which prints:
```
Finding all available ports for the MotorsBus.
["/dev/tty.usbmodem575E0032081", "/dev/tty.usbmodem575E0031751"]
Remove the usb cable from your MotorsBus and press Enter when done.
The port of this MotorsBus is /dev/tty.usbmodem575E0031751.
Reconnect the usb cable.
```
Example of usage for 1 Feetech sts3215 motor connected to the bus:
@@ -595,7 +600,7 @@ class SerialMotorsBus(MotorsBusBase):
ID, and finally programs the bus' default baud-rate.
Args:
motor (str): Key of the motor in :pyattr:`motors`.
motor (str): Key of the motor in `motors`.
initial_baudrate (int | None, optional): Current baud-rate (skips scanning when provided).
Defaults to None.
initial_id (int | None, optional): Current ID (skips scanning when provided). Defaults to None.
@@ -666,7 +671,7 @@ class SerialMotorsBus(MotorsBusBase):
"""Enable torque on selected motors.
Args:
motors (int | str | list[str] | None, optional): Same semantics as :pymeth:`disable_torque`.
motors (int | str | list[str] | None, optional): Same semantics as [`~motors.motors_bus.MotorsBus.disable_torque`].
Defaults to `None`.
num_retry (int, optional): Number of additional retry attempts on communication failure.
Defaults to 0.
@@ -679,10 +684,12 @@ class SerialMotorsBus(MotorsBusBase):
This helper is useful to temporarily disable torque when configuring motors.
Examples:
>>> with bus.torque_disabled():
Example:
```python
>>> with bus.torque_disabled(): # doctest: +SKIP
... # Safe operations here
... pass
```
"""
self.disable_torque(motors)
try:
@@ -695,7 +702,7 @@ class SerialMotorsBus(MotorsBusBase):
Args:
timeout_ms (int | None, optional): Timeout in *milliseconds*. If `None` (default) the method falls
back to :pyattr:`default_timeout`.
back to `default_timeout`.
"""
timeout_ms = timeout_ms if timeout_ms is not None else self.default_timeout
self.port_handler.setPacketTimeoutMillis(timeout_ms)
@@ -746,8 +753,8 @@ class SerialMotorsBus(MotorsBusBase):
Args:
calibration_dict (dict[str, MotorCalibration]): Calibration obtained from
:pymeth:`read_calibration` or crafted by the user.
cache (bool, optional): Save the calibration to :pyattr:`calibration`. Defaults to True.
[`~motors.motors_bus.MotorsBus.read_calibration`] or crafted by the user.
cache (bool, optional): Save the calibration to `calibration`. Defaults to True.
"""
pass
@@ -755,7 +762,7 @@ class SerialMotorsBus(MotorsBusBase):
"""Restore factory calibration for the selected motors.
Homing offset is set to ``0`` and min/max position limits are set to the full usable range.
The in-memory :pyattr:`calibration` is cleared.
The in-memory `calibration` is cleared.
Args:
motors (NameOrID | Sequence[NameOrID] | None, optional): Selection of motors. `None` (default)
@@ -1069,9 +1076,9 @@ class SerialMotorsBus(MotorsBusBase):
) -> None:
"""Write a value to a single motor's register.
Contrary to :pymeth:`sync_write`, this expects a response status packet emitted by the motor, which
Contrary to [`~motors.motors_bus.MotorsBus.sync_write`], this expects a response status packet emitted by the motor, which
provides a guarantee that the value was written to the register successfully. In consequence, it is
slower than :pymeth:`sync_write` but it is more reliable. It should typically be used when configuring
slower than [`~motors.motors_bus.MotorsBus.sync_write`] but it is more reliable. It should typically be used when configuring
motors.
Args:
@@ -1228,8 +1235,8 @@ class SerialMotorsBus(MotorsBusBase):
) -> None:
"""Write the same register on multiple motors.
Contrary to :pymeth:`write`, this *does not* expects a response status packet emitted by the motor, which
can allow for lost packets. It is faster than :pymeth:`write` and should typically be used when
Contrary to [`~motors.motors_bus.MotorsBus.write`], this *does not* expects a response status packet emitted by the motor, which
can allow for lost packets. It is faster than [`~motors.motors_bus.MotorsBus.write`] and should typically be used when
frequency matters and losing some packets is acceptable (e.g. teleoperation loops).
Args:
+50 -42
View File
@@ -58,6 +58,16 @@ logger = logging.getLogger(__name__)
class MotorState(TypedDict):
"""A Robstride motor's last-reported feedback frame.
**Attributes**:
- **position** (`float`) -- Position, in radians.
- **velocity** (`float`) -- Velocity, in radians per second.
- **torque** (`float`) -- Torque, in newton-meters.
- **temp_mos** (`float`) -- MOSFET temperature, in degrees Celsius.
- **temp_rotor** (`float`) -- Rotor temperature, in degrees Celsius.
"""
position: float
velocity: float
torque: float
@@ -66,8 +76,7 @@ class MotorState(TypedDict):
class RobstrideMotorsBus(MotorsBusBase):
"""
The Robstride implementation for a MotorsBus using CAN bus communication.
"""The Robstride implementation for a MotorsBus using CAN bus communication.
This class uses python-can for CAN bus communication with Robstride motors.
The motors need to be switched to MIT control mode to be compatible with this implementation.
@@ -95,8 +104,7 @@ class RobstrideMotorsBus(MotorsBusBase):
bitrate: int = 1000000,
data_bitrate: int | None = 5000000,
):
"""
Initialize the Robstride motors bus.
"""Initialize the Robstride motors bus.
Args:
port: CAN interface name (e.g., "can0" for Linux, "/dev/cu.usbmodem*" for macOS)
@@ -174,8 +182,7 @@ class RobstrideMotorsBus(MotorsBusBase):
@check_if_already_connected
def connect(self, handshake: bool = True) -> None:
"""
Open the CAN bus and initialize communication.
"""Open the CAN bus and initialize communication.
Args:
handshake: If True, ping all motors to verify they're present
@@ -230,8 +237,7 @@ class RobstrideMotorsBus(MotorsBusBase):
def _recv_status_via_clear_fault(
self, expected_recv_id: int | None = None, timeout: float = RUNNING_TIMEOUT
) -> tuple[bool, can.Message | None]:
"""
Poll the bus for a response to a fault-clear request.
"""Poll the bus for a response to a fault-clear request.
Args:
expected_recv_id: Only accept frames from this CAN ID when provided.
@@ -266,6 +272,19 @@ class RobstrideMotorsBus(MotorsBusBase):
return False, None
def update_motor_state(self, motor: NameOrID) -> bool:
"""Query one motor's status and refresh its cached `MotorState`.
Args:
motor (`str | int`):
Motor name or ID to query.
Returns:
`bool`: `True` once the cached state has been refreshed.
Raises:
ConnectionError: If the motor does not respond.
RuntimeError: If the motor reports a fault.
"""
has_fault, msg = self._query_status_via_clear_fault(motor)
if msg is None:
logger.warning(f"No response received from motor '{motor}' during state update.")
@@ -327,8 +346,7 @@ class RobstrideMotorsBus(MotorsBusBase):
@check_if_not_connected
def disconnect(self, disable_torque: bool = True) -> None:
"""
Close the CAN bus connection.
"""Close the CAN bus connection.
Args:
disable_torque: If True, disable torque on all motors before disconnecting
@@ -407,8 +425,7 @@ class RobstrideMotorsBus(MotorsBusBase):
@contextmanager
def torque_disabled(self, motors: str | list[str] | None = None):
"""
Context manager that guarantees torque is re-enabled.
"""Context manager that guarantees torque is re-enabled.
This helper is useful to temporarily disable torque when configuring motors.
@@ -438,8 +455,7 @@ class RobstrideMotorsBus(MotorsBusBase):
def _recv_motor_response(
self, expected_recv_id: int | None = None, timeout: float = 0.001
) -> can.Message | None:
"""
Receive a response from a motor.
"""Receive a response from a motor.
Args:
expected_recv_id: If provided, only return messages from this CAN ID
@@ -481,8 +497,8 @@ class RobstrideMotorsBus(MotorsBusBase):
def _recv_all_responses(
self, expected_recv_ids: list[int], timeout: float = 0.002
) -> dict[int, can.Message]:
"""
Efficiently receive responses from multiple motors at once.
"""Efficiently receive responses from multiple motors at once.
Uses the OpenArms pattern: collect all available messages within timeout.
Args:
@@ -514,8 +530,7 @@ class RobstrideMotorsBus(MotorsBusBase):
timeout: float = RUNNING_TIMEOUT,
max_messages: int = 4096,
) -> list[can.Message]:
"""
Receive frames until the bus goes quiet.
"""Receive frames until the bus goes quiet.
Args:
timeout: Poll timeout used for each recv() call. Collection stops
@@ -538,8 +553,7 @@ class RobstrideMotorsBus(MotorsBusBase):
return out
def _process_feedback_messages(self, messages: list[can.Message]) -> set[int]:
"""
Decode all received feedback frames and update cached motor states.
"""Decode all received feedback frames and update cached motor states.
Returns:
Set of payload recv_ids that were successfully mapped to motors.
@@ -568,8 +582,7 @@ class RobstrideMotorsBus(MotorsBusBase):
return processed_recv_ids
def flush_rx_queue(self, poll_timeout_s: float = 0.0005, max_messages: int = 4096) -> int:
"""
Drain pending RX frames from the CAN interface.
"""Drain pending RX frames from the CAN interface.
This is used by higher-level controllers to drop stale feedback before issuing
a fresh read cycle, so subsequent state reads are based on most recent replies.
@@ -595,12 +608,11 @@ class RobstrideMotorsBus(MotorsBusBase):
velocity_deg_per_sec: float,
current_limit_a: float,
) -> None:
"""
Send a Velocity Mode Control Command (Command 11) to a single motor.
"""Send a Velocity Mode Control Command (Command 11) to a single motor.
Args:
motor: Motor name or CAN ID.
velocity_rad_per_sec: Target speed in rad/s (32-bit float).
velocity_deg_per_sec: Target speed in degrees/s (converted to rad/s on the wire, 32-bit float).
current_limit_a: Current limit in A (32-bit float).
"""
if not self.is_connected:
@@ -651,8 +663,7 @@ class RobstrideMotorsBus(MotorsBusBase):
*,
wait_for_response: bool = True,
) -> None:
"""
Send MIT control command to a motor.
"""Send MIT control command to a motor.
Args:
motor: Motor name or ID
@@ -661,6 +672,7 @@ class RobstrideMotorsBus(MotorsBusBase):
position_degrees: Target position (degrees)
velocity_deg_per_sec: Target velocity (degrees/s)
torque: Target torque (N·m)
wait_for_response: Whether to wait for and decode the motor's state response.
"""
motor_name = self._get_motor_name(motor)
motor_type = self._motor_types[motor_name]
@@ -751,8 +763,7 @@ class RobstrideMotorsBus(MotorsBusBase):
return data_norm * span + x_min
def _decode_motor_state(self, data: bytearray | bytes) -> tuple[float, float, float, float]:
"""
Decode motor state from CAN data.
"""Decode motor state from CAN data.
Returns:
Tuple of (position_degrees, velocity_deg_per_sec, torque, temp_mos)
@@ -825,7 +836,6 @@ class RobstrideMotorsBus(MotorsBusBase):
motor: str,
) -> Value:
"""Read a value from a single motor. Positions are always in degrees."""
# Refresh motor to get latest state
t_init = time.time()
if (
@@ -867,10 +877,10 @@ class RobstrideMotorsBus(MotorsBusBase):
data_name: str,
motors: str | list[str] | None = None,
) -> dict[str, Value]:
"""
Read the same value from multiple motors simultaneously.
Uses batched operations: sends all refresh commands, then collects all responses.
This is MUCH faster than sequential reads (OpenArms pattern).
"""Read the same value from multiple motors simultaneously.
Uses batched operations: sends all refresh commands, then collects all responses. Much faster
than sequential reads (the OpenArms pattern).
"""
target_motors = self._get_motors_list(motors)
self._batch_refresh(target_motors)
@@ -882,9 +892,10 @@ class RobstrideMotorsBus(MotorsBusBase):
data_name: str,
values: dict[str, Value],
) -> None:
"""
Write different values to multiple motors simultaneously. Positions are always in degrees.
Uses batched operations: sends all commands first, then collects responses when MIT mode is used, otherwise send cmd and wait for response for each motor).
"""Write different values to multiple motors simultaneously. Positions are always in degrees.
Uses batched operations: sends all commands first, then collects responses when MIT mode is used;
otherwise sends the command and waits for a response for each motor individually.
"""
if data_name in ("Kp", "Kd"):
key = data_name.lower()
@@ -914,9 +925,7 @@ class RobstrideMotorsBus(MotorsBusBase):
*,
num_retry: int = 0,
) -> dict[str, MotorState]:
"""
Read ALL motor states (position, velocity, torque) with Robstride TTL refresh policy.
"""
"""Read ALL motor states (position, velocity, torque) with Robstride TTL refresh policy."""
target_motors = self._get_motors_list(motors)
self._batch_refresh(target_motors)
return {motor: self._last_known_states[motor].copy() for motor in target_motors}
@@ -962,8 +971,7 @@ class RobstrideMotorsBus(MotorsBusBase):
def record_ranges_of_motion(
self, motors: str | list[str] | None = None, display_values: bool = True
) -> tuple[dict[str, Value], dict[str, Value]]:
"""
Interactively record the min/max values of each motor in degrees.
"""Interactively record the min/max values of each motor in degrees.
Move the joints by hand (with torque disabled) while the method streams live positions.
Press Enter to finish.
+19 -1
View File
@@ -12,13 +12,15 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Configuration tables for Damiao motors."""
"""Configuration tables for Robstride motors."""
from enum import IntEnum
# Motor type definitions
class MotorType(IntEnum):
"""The Robstride motor model, selecting its limit parameters."""
O0 = 0
O1 = 1
O2 = 2
@@ -30,6 +32,14 @@ class MotorType(IntEnum):
class CommMode(IntEnum):
"""The CAN communication protocol a Robstride motor speaks.
**Attributes**:
- **PrivateProtocole** -- Robstride's own CAN frame format.
- **CANopen** -- The standard CANopen protocol.
- **MIT** -- The MIT Cheetah CAN protocol.
"""
PrivateProtocole = 0
CANopen = 1
MIT = 2
@@ -37,6 +47,14 @@ class CommMode(IntEnum):
# Control modes
class ControlMode(IntEnum):
"""The Robstride motor's active control mode.
**Attributes**:
- **MIT** -- Combined position/velocity/torque control, following the MIT Cheetah protocol.
- **POS_VEL** -- Position and velocity control.
- **VEL** -- Velocity-only control.
"""
MIT = 0
POS_VEL = 1
VEL = 2
+10 -6
View File
@@ -131,12 +131,16 @@ class ProcessorConfigKwargs(TypedDict, total=False):
This provides type hints for the optional arguments passed to `make_pre_post_processors`,
improving code clarity and enabling static analysis.
Attributes:
preprocessor_config_filename: The filename for the preprocessor configuration.
postprocessor_config_filename: The filename for the postprocessor configuration.
preprocessor_overrides: A dictionary of overrides for the preprocessor configuration.
postprocessor_overrides: A dictionary of overrides for the postprocessor configuration.
dataset_stats: Dataset statistics for normalization.
**Attributes**:
- **preprocessor_config_filename** (`str | None`) -- The filename for the preprocessor configuration.
- **postprocessor_config_filename** (`str | None`) -- The filename for the postprocessor
configuration.
- **preprocessor_overrides** (`dict[str, Any] | None`) -- A dictionary of overrides for the
preprocessor configuration.
- **postprocessor_overrides** (`dict[str, Any] | None`) -- A dictionary of overrides for the
postprocessor configuration.
- **dataset_stats** (`dict[str, dict[str, torch.Tensor]] | None`) -- Dataset statistics for
normalization.
"""
preprocessor_config_filename: str | None
+1 -47
View File
@@ -28,8 +28,7 @@ from huggingface_hub.errors import HfHubHTTPError
from safetensors.torch import load_model as load_model_as_safetensor
from torch import Tensor, nn
from lerobot.configs import PreTrainedConfig, TextKind
from lerobot.utils.constants import ACTION
from lerobot.configs import PreTrainedConfig
from lerobot.utils.device_utils import resolve_safetensors_device
from lerobot.utils.hub import HubMixin
from lerobot.utils.import_utils import _peft_available, require_package
@@ -211,51 +210,6 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
"""
raise NotImplementedError
def drop_queued_actions(self) -> None:
"""Discard actions precomputed by earlier ``select_action`` calls.
Chunking policies answer most control ticks from a queue filled by an
earlier forward pass, so a mid-episode change to the conditioning
e.g. a new language instruction would otherwise only take effect
once that queue drains (up to ``chunk_size`` ticks). Dropping the
queue forces a fresh forward pass on the next ``select_action``.
Unlike :meth:`reset` this keeps the rest of the episode state (e.g.
observation history), so it does not perturb policies that condition
on it. Call it from the thread that calls ``select_action``: it
mutates the same queues that thread pops from.
Policies that keep no action queue inherit a no-op.
"""
queues = getattr(self, "_queues", None)
if isinstance(queues, dict) and ACTION in queues:
queues[ACTION].clear()
action_queue = getattr(self, "_action_queue", None)
if action_queue is not None:
action_queue.clear()
def supports_text_generation(self) -> bool:
"""Whether this policy implements the optional :meth:`generate_text` hook."""
return type(self).generate_text is not PreTrainedPolicy.generate_text
def generate_text(
self,
batch: dict[str, Tensor],
*,
kind: TextKind = TextKind.SUBTASK,
user_text: str | None = None,
) -> str:
"""Generate one string from a policy's optional language head.
Interactive rollout calls this with a policy-ready observation batch.
Implementations must treat the batch as read-only and avoid mutating
action queues or episode state: text generation runs on a background
worker while the control loop remains active.
"""
raise NotImplementedError(
f"{type(self).__name__} has no text head. Implement `generate_text` to support /ask."
)
def supports_rtc(self) -> bool:
"""Whether this policy implements Real-Time Chunking inference semantics."""
return False
+5 -4
View File
@@ -46,10 +46,11 @@ class ActionQueue:
Args:
cfg (RTCConfig): Configuration for Real-Time Chunking behavior.
Attributes:
queue (Tensor | None): Processed actions for robot rollout (time_steps, action_dim).
original_queue (Tensor | None): Original actions for RTC computation (time_steps, action_dim).
last_index (int): Current consumption index in the queue.
**Attributes**:
- **queue** (`Tensor | None`) -- Processed actions for robot rollout (time_steps, action_dim).
- **original_queue** (`Tensor | None`) -- Original actions for RTC computation (time_steps,
action_dim).
- **last_index** (`int`) -- Current consumption index in the queue.
"""
def __init__(self, cfg: RTCConfig):
+13 -13
View File
@@ -27,19 +27,19 @@ from torch import Tensor
class DebugStep:
"""Container for debug information from a single denoising step.
Attributes:
step_idx (int): Step index/counter.
x_t (Tensor | None): Current latent/state tensor.
v_t (Tensor | None): Velocity from denoiser.
x1_t (Tensor | None): Denoised prediction (x_t - time * v_t).
correction (Tensor | None): Correction gradient tensor.
err (Tensor | None): Weighted error term.
weights (Tensor | None): Prefix attention weights.
guidance_weight (float | Tensor | None): Applied guidance weight.
time (float | Tensor | None): Time parameter.
inference_delay (int | None): Inference delay parameter.
execution_horizon (int | None): Execution horizon parameter.
metadata (dict[str, Any]): Additional metadata.
**Attributes**:
- **step_idx** (`int`) -- Step index/counter.
- **x_t** (`Tensor | None`) -- Current latent/state tensor.
- **v_t** (`Tensor | None`) -- Velocity from denoiser.
- **x1_t** (`Tensor | None`) -- Denoised prediction (x_t - time * v_t).
- **correction** (`Tensor | None`) -- Correction gradient tensor.
- **err** (`Tensor | None`) -- Weighted error term.
- **weights** (`Tensor | None`) -- Prefix attention weights.
- **guidance_weight** (`float | Tensor | None`) -- Applied guidance weight.
- **time** (`float | Tensor | None`) -- Time parameter.
- **inference_delay** (`int | None`) -- Inference delay parameter.
- **execution_horizon** (`int | None`) -- Execution horizon parameter.
- **metadata** (`dict[str, Any]`) -- Additional metadata.
"""
step_idx: int = 0
@@ -52,7 +52,6 @@ from torch.nn import CrossEntropyLoss
from torchvision.transforms import InterpolationMode
from torchvision.transforms.v2 import functional as tv_functional
from lerobot.configs import TextKind
from lerobot.utils.constants import ACTION, OBS_STATE
from lerobot.utils.import_utils import (
_wallx_deps_available,
@@ -108,7 +107,6 @@ else:
from .utils import (
get_wallx_normal_text,
img_key_mapping,
preprocesser_call,
process_grounding_points,
replace_action_token,
@@ -1587,25 +1585,6 @@ class Qwen2_5_VLMoEForAction(_Qwen2_5_VLForAction_Base): # noqa: N801
- Handles special cases for input_embeds, generation methods, and GPU synchronization
- Manages vision inputs to avoid unnecessary forward passes
"""
if cache_position is None:
past_length = 0
if past_key_values is not None and hasattr(past_key_values, "get_seq_length"):
past_length = int(past_key_values.get_seq_length())
input_length = input_ids.shape[1]
end = input_length if input_length > past_length else past_length + input_length
cache_position = torch.arange(
past_length,
end,
dtype=torch.long,
device=input_ids.device,
)
if cache_position.numel() == 0:
cache_position = torch.arange(
input_length,
dtype=torch.long,
device=input_ids.device,
)
# Initialize MoE token types if not provided
if moe_token_types is None:
moe_token_types = torch.zeros_like(
@@ -1872,23 +1851,6 @@ class WallXPolicy(PreTrainedPolicy):
"""Get parameters for optimization."""
return self.parameters()
@staticmethod
def _observation_prompt(img_keys: list[str]) -> str:
prompt = "Observation:"
for label in img_key_mapping(img_keys):
prompt += f" {label}: <|vision_start|><|image_pad|><|vision_end|>"
return prompt
def _format_text_prompt(self, instruction: str, kind: str, img_keys: list[str]) -> str:
if kind == TextKind.SUBTASK:
instruction = f"{instruction}\nPredict the next action in language."
return (
"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
f"<|im_start|>user\n{self._observation_prompt(img_keys)}\n"
f"Instruction: {instruction}<|im_end|>\n"
"<|im_start|>assistant\n"
)
def preprocess_inputs(
self,
batch: dict[str, Any],
@@ -2118,118 +2080,6 @@ class WallXPolicy(PreTrainedPolicy):
return loss, loss_dict
def _build_text_inputs(
self,
batch: dict[str, Any],
*,
kind: str,
user_text: str | list[str] | None,
) -> BatchFeature:
batch_size = batch[OBS_STATE].shape[0]
img_keys = [key for key in self.config.image_features if key in batch]
if not img_keys:
raise ValueError("Wall-X text generation requires at least one image feature.")
image_inputs, dimensions_by_key = _prepare_wall_x_image_inputs(batch, img_keys)
orig_height, orig_width, resized_height, resized_width = dimensions_by_key[img_keys[-1]]
tasks = batch["task"] if isinstance(batch["task"], list) else [batch["task"]] * batch_size
if user_text is None:
instructions = tasks
elif isinstance(user_text, str):
instructions = [user_text] * batch_size
elif len(user_text) == batch_size:
instructions = user_text
else:
raise ValueError(f"Expected one text prompt for each of the {batch_size} samples.")
texts = [
process_grounding_points(
self._format_text_prompt(str(instruction), kind, img_keys),
orig_height,
orig_width,
resized_height,
resized_width,
MODEL_TYPE,
)
for instruction in instructions
]
inputs = preprocesser_call(
processor=self.model.processor,
text=texts,
images=image_inputs,
videos=None,
device=batch[OBS_STATE].device,
padding=True,
truncation=True,
return_tensors="pt",
max_length=TOKENIZER_MAX_LENGTH,
)
inputs.pop("labels", None)
inputs["moe_token_types"] = torch.zeros_like(inputs.input_ids, dtype=torch.bool)
for key, value in inputs.items():
if isinstance(value, torch.Tensor):
inputs[key] = value.to(batch[OBS_STATE].device)
return inputs
@torch.no_grad()
def generate_text(
self,
batch: dict[str, Tensor],
*,
kind: TextKind = TextKind.SUBTASK,
user_text: str | None = None,
) -> str:
"""Generate one grounded language response from the WALL-OSS VLM."""
outputs = self.generate_texts(
batch,
kind=kind,
user_text=user_text,
temperature=self.config.text_temperature,
top_p=self.config.text_top_p,
)
if len(outputs) != 1:
raise ValueError(f"Interactive rollout expected one Wall-X output, got {len(outputs)}.")
return outputs[0]
@torch.no_grad()
def generate_texts(
self,
batch: dict[str, Any],
*,
kind: TextKind = TextKind.VQA,
user_text: str | list[str] | None = None,
max_new_tokens: int = 100,
min_new_tokens: int = 0,
temperature: float = 0.0,
top_p: float = 1.0,
) -> list[str]:
"""Generate grounded Wall-X text for one or more observations."""
self.eval()
if kind not in {TextKind.VQA, TextKind.SUBTASK}:
raise ValueError("Unsupported Wall-X text kind.")
inputs = self._build_text_inputs(batch, kind=kind, user_text=user_text)
prompt_length = inputs.input_ids.shape[1]
sampling = temperature > 0
generation_kwargs: dict[str, Any] = {
"max_new_tokens": max_new_tokens,
"min_new_tokens": min_new_tokens,
"do_sample": sampling,
"eos_token_id": self.model.processor.tokenizer.eos_token_id,
"pad_token_id": self.model.processor.tokenizer.pad_token_id,
"use_cache": True,
}
if sampling:
generation_kwargs.update(temperature=temperature, top_p=top_p)
output_ids = self.model.generate(**inputs, **generation_kwargs)
return [
value.strip()
for value in self.model.processor.tokenizer.batch_decode(
output_ids[:, prompt_length:],
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
)
]
@torch.no_grad()
def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
"""Predict action chunk for evaluation."""
+6 -4
View File
@@ -217,10 +217,12 @@ class AddBatchDimensionProcessorStep(ProcessorStep):
This step combines individual processors for actions, observations, and complementary data
to create a batched transition (batch size 1) from a single-instance transition.
Attributes:
to_batch_action_processor: Processor for the action component.
to_batch_observation_processor: Processor for the observation component.
to_batch_complementary_data_processor: Processor for the complementary data component.
**Attributes**:
- **to_batch_action_processor** (`AddBatchDimensionActionStep`) -- Processor for the action component.
- **to_batch_observation_processor** (`AddBatchDimensionObservationStep`) -- Processor for the
observation component.
- **to_batch_complementary_data_processor** (`AddBatchDimensionComplementaryDataStep`) -- Processor
for the complementary data component.
"""
to_batch_action_processor: AddBatchDimensionActionStep = field(
@@ -32,9 +32,8 @@ class MapTensorToDeltaActionDictStep(ActionProcessorStep):
It decomposes the vector into named components for delta movements of the
end-effector (x, y, z) and optionally the gripper.
Attributes:
use_gripper: If True, assumes the 4th element of the tensor is the
gripper action.
**Attributes**:
- **use_gripper** (`bool`) -- If True, assumes the 4th element of the tensor is the gripper action.
"""
use_gripper: bool = True
@@ -81,10 +80,10 @@ class MapDeltaActionToRobotActionStep(RobotActionProcessorStep):
into a target action format that includes an "enabled" flag and target
end-effector positions. It also handles scaling and noise filtering.
Attributes:
position_scale: A factor to scale the delta position inputs.
noise_threshold: The magnitude below which delta inputs are considered noise
and do not trigger an "enabled" state.
**Attributes**:
- **position_scale** (`float`) -- A factor to scale the delta position inputs.
- **noise_threshold** (`float`) -- The magnitude below which delta inputs are considered noise and do
not trigger an "enabled" state.
"""
# Scale factors for delta movements
+4 -4
View File
@@ -40,10 +40,10 @@ class DeviceProcessorStep(ProcessorStep):
This is crucial for preparing data for model training or inference on hardware like GPUs.
Attributes:
device: The target device for tensors (e.g., "cpu", "cuda", "cuda:0").
float_dtype: The target floating-point dtype as a string (e.g., "float32", "float16", "bfloat16").
If None, the dtype is not changed.
**Attributes**:
- **device** (`str`) -- The target device for tensors (e.g., "cpu", "cuda", "cuda:0").
- **float_dtype** (`str | None`) -- The target floating-point dtype as a string (e.g., "float32",
"float16", "bfloat16"). If None, the dtype is not changed.
"""
device: str = "cpu"
@@ -33,10 +33,9 @@ class Torch2NumpyActionProcessorStep(ActionProcessorStep):
This step is useful when the output of a policy (typically a torch.Tensor)
needs to be passed to an environment or component that expects a NumPy array.
Attributes:
squeeze_batch_dim: If True, removes the first dimension of the array
if it is of size 1. This is useful for converting a
batched action of size (1, D) to a single action of size (D,).
**Attributes**:
- **squeeze_batch_dim** (`bool`) -- If True, removes the first dimension of the array if it is of size
1. This is useful for converting a batched action of size (1, D) to a single action of size (D,).
"""
squeeze_batch_dim: bool = True
+28 -28
View File
@@ -101,8 +101,8 @@ class AddTeleopActionAsComplimentaryDataStep(ComplementaryDataProcessorStep):
be available to downstream processors, for example, to override a policy's action
during an intervention.
Attributes:
teleop_device: The teleoperator instance to get the action from.
**Attributes**:
- **teleop_device** (`Teleoperator`) -- The teleoperator instance to get the action from.
"""
teleop_device: "Teleoperator"
@@ -137,9 +137,9 @@ class AddTeleopEventsAsInfoStep(InfoProcessorStep):
This step extracts control events from teleoperators that support event-based
interaction, making these signals available to other parts of the system.
Attributes:
teleop_device: An instance of a teleoperator that implements the
`HasTeleopEvents` protocol.
**Attributes**:
- **teleop_device** (`TeleopWithEvents`) -- An instance of a teleoperator that implements the
`HasTeleopEvents` protocol.
"""
teleop_device: TeleopWithEvents
@@ -180,10 +180,10 @@ class ImageCropResizeProcessorStep(ObservationProcessorStep):
the specified transformations. It handles device placement, moving tensors to the
CPU if necessary for operations not supported on certain accelerators like MPS.
Attributes:
crop_params_dict: A dictionary mapping image keys to cropping parameters
(top, left, height, width).
resize_size: A tuple (height, width) to resize all images to.
**Attributes**:
- **crop_params_dict** (`dict[str, tuple[int, int, int, int]] | None`) -- A dictionary mapping image
keys to cropping parameters (top, left, height, width).
- **resize_size** (`tuple[int, int] | None`) -- A tuple (height, width) to resize all images to.
"""
crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None
@@ -267,9 +267,9 @@ class TimeLimitProcessorStep(TruncatedProcessorStep):
"""
Tracks episode steps and enforces a time limit by truncating the episode.
Attributes:
max_episode_steps: The maximum number of steps allowed per episode.
current_step: The current step count for the active episode.
**Attributes**:
- **max_episode_steps** (`int`) -- The maximum number of steps allowed per episode.
- **current_step** (`int`) -- The current step count for the active episode.
"""
max_episode_steps: int
@@ -358,11 +358,11 @@ class GripperPenaltyProcessorStep(ProcessorStep):
This discourages gripper oscillation while leaving "stay" and saturating-further
commands unpenalized.
Attributes:
penalty: The negative reward value to apply.
max_gripper_pos: The maximum position value for the gripper, used for normalization.
open_threshold: Normalized state below which the gripper is considered "open".
closed_threshold: Normalized state above which the gripper is considered "closed".
**Attributes**:
- **penalty** (`float`) -- The negative reward value to apply.
- **max_gripper_pos** (`float`) -- The maximum position value for the gripper, used for normalization.
- **open_threshold** (`float`) -- Normalized state below which the gripper is considered "open".
- **closed_threshold** (`float`) -- Normalized state above which the gripper is considered "closed".
"""
penalty: float = -0.02
@@ -456,10 +456,10 @@ class InterventionActionProcessorStep(ProcessorStep):
this step replaces the policy's action with the human's teleoperated action.
It also processes signals to terminate the episode or flag success.
Attributes:
use_gripper: Whether to include the gripper in the teleoperated action.
terminate_on_success: If True, automatically sets the `done` flag when a
`success` event is received.
**Attributes**:
- **use_gripper** (`bool`) -- Whether to include the gripper in the teleoperated action.
- **terminate_on_success** (`bool`) -- If True, automatically sets the `done` flag when a `success`
event is received.
"""
use_gripper: bool = False
@@ -557,13 +557,13 @@ class RewardClassifierProcessorStep(ProcessorStep):
This step uses a model to determine if the current state is successful, updating
the reward and potentially terminating the episode.
Attributes:
pretrained_path: Path to the pretrained reward classifier model.
device: The device to run the classifier on.
success_threshold: The probability threshold to consider a prediction as successful.
success_reward: The reward value to assign on success.
terminate_on_success: If True, terminates the episode upon successful classification.
reward_classifier: The loaded classifier model instance.
**Attributes**:
- **pretrained_path** (`str | None`) -- Path to the pretrained reward classifier model.
- **device** (`str`) -- The device to run the classifier on.
- **success_threshold** (`float`) -- The probability threshold to consider a prediction as successful.
- **success_reward** (`float`) -- The reward value to assign on success.
- **terminate_on_success** (`bool`) -- If True, terminates the episode upon successful classification.
- **reward_classifier** (`Any`) -- The loaded classifier model instance.
"""
pretrained_path: str | None = None
+17 -16
View File
@@ -71,22 +71,23 @@ class _NormalizationMixin:
)
```
Attributes:
features: A dictionary mapping feature names to `PolicyFeature` objects, defining
the data structure to be processed.
norm_map: A dictionary mapping `FeatureType` to `NormalizationMode`, specifying
which normalization method to use for each type of feature.
stats: A dictionary containing the normalization statistics (e.g., mean, std,
min, max) for each feature.
device: The PyTorch device on which to store and perform tensor operations.
eps: A small epsilon value to prevent division by zero in normalization
calculations.
normalize_observation_keys: An optional set of keys to selectively apply
normalization to specific observation features.
_tensor_stats: An internal dictionary holding the normalization statistics as
PyTorch tensors.
_stats_explicitly_provided: Internal flag tracking whether stats were explicitly
provided during construction (used for override preservation).
**Attributes**:
- **features** (`dict[str, PolicyFeature]`) -- A dictionary mapping feature names to `PolicyFeature`
objects, defining the data structure to be processed.
- **norm_map** (`dict[FeatureType, NormalizationMode]`) -- A dictionary mapping `FeatureType` to
`NormalizationMode`, specifying which normalization method to use for each type of feature.
- **stats** (`dict[str, dict[str, Any]] | None`) -- A dictionary containing the normalization
statistics (e.g., mean, std, min, max) for each feature.
- **device** (`torch.device | str | None`) -- The PyTorch device on which to store and perform tensor
operations.
- **eps** (`float`) -- A small epsilon value to prevent division by zero in normalization
calculations.
- **normalize_observation_keys** (`set[str] | None`) -- An optional set of keys to selectively apply
normalization to specific observation features.
- **_tensor_stats** (`dict[str, dict[str, Tensor]]`) -- An internal dictionary holding the
normalization statistics as PyTorch tensors.
- **_stats_explicitly_provided** (`bool`) -- Internal flag tracking whether stats were explicitly
provided during construction (used for override preservation).
"""
features: dict[str, PolicyFeature]
+12 -7
View File
@@ -269,13 +269,18 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
data processing workflow. It's generic, allowing for custom input and output types,
which are handled by the `to_transition` and `to_output` converters.
Attributes:
steps: A sequence of `ProcessorStep` objects that make up the pipeline.
name: A descriptive name for the pipeline.
to_transition: A function to convert raw input data into the standardized `EnvTransition` format.
to_output: A function to convert the final `EnvTransition` into the desired output format.
before_step_hooks: A list of functions to be called before each step is executed.
after_step_hooks: A list of functions to be called after each step is executed.
**Attributes**:
- **steps** (`Sequence[ProcessorStep]`) -- A sequence of `ProcessorStep` objects that make up the
pipeline.
- **name** (`str`) -- A descriptive name for the pipeline.
- **to_transition** (`Callable[[TInput], EnvTransition]`) -- A function to convert raw input data into
the standardized `EnvTransition` format.
- **to_output** (`Callable[[EnvTransition], TOutput]`) -- A function to convert the final
`EnvTransition` into the desired output format.
- **before_step_hooks** (`list[Callable[[int, EnvTransition], None]]`) -- A list of functions to be
called before each step is executed.
- **after_step_hooks** (`list[Callable[[int, EnvTransition], None]]`) -- A list of functions to be
called after each step is executed.
"""
steps: Sequence[ProcessorStep] = field(default_factory=list)
@@ -91,11 +91,11 @@ class RelativeActionsProcessorStep(ProcessorStep):
Caches the last seen state so a paired AbsoluteActionsProcessorStep can reverse
the conversion during postprocessing.
Attributes:
enabled: Whether to apply the relative conversion.
exclude_joints: Joint names to keep absolute (not converted to relative).
action_names: Action dimension names from dataset metadata, used to build
the mask from exclude_joints. If None, all dims are converted.
**Attributes**:
- **enabled** (`bool`) -- Whether to apply the relative conversion.
- **exclude_joints** (`list[str]`) -- Joint names to keep absolute (not converted to relative).
- **action_names** (`list[str] | None`) -- Action dimension names from dataset metadata, used to build
the mask from exclude_joints. If None, all dims are converted.
"""
enabled: bool = False
@@ -168,9 +168,10 @@ class AbsoluteActionsProcessorStep(ProcessorStep):
predicted relative offsets are converted back to absolute positions for execution.
Reads the cached state from its paired RelativeActionsProcessorStep.
Attributes:
enabled: Whether to apply the absolute conversion.
relative_step: Reference to the paired RelativeActionsProcessorStep that caches state.
**Attributes**:
- **enabled** (`bool`) -- Whether to apply the absolute conversion.
- **relative_step** (`RelativeActionsProcessorStep | None`) -- Reference to the paired
RelativeActionsProcessorStep that caches state.
"""
enabled: bool = False
+3 -4
View File
@@ -32,10 +32,9 @@ class RenameObservationsProcessorStep(ObservationProcessorStep):
from an environment's format to the format expected by a LeRobot policy or
other downstream components.
Attributes:
rename_map: A dictionary mapping from old key names to new key names.
Keys present in an observation that are not in this map will
be kept with their original names.
**Attributes**:
- **rename_map** (`dict[str, str]`) -- A dictionary mapping from old key names to new key names. Keys
present in an observation that are not in this map will be kept with their original names.
"""
rename_map: dict[str, str] = field(default_factory=dict)
+22 -15
View File
@@ -65,15 +65,17 @@ class TokenizerProcessorStep(ObservationProcessorStep):
Requires the `transformers` library to be installed.
Attributes:
tokenizer_name: The name of a pretrained tokenizer from the Hugging Face Hub (e.g., "bert-base-uncased").
tokenizer: A pre-initialized tokenizer object. If provided, `tokenizer_name` is ignored.
max_length: The maximum length to pad or truncate sequences to.
task_key: The key in `complementary_data` where the task string is stored.
padding_side: The side to pad on ('left' or 'right').
padding: The padding strategy ('max_length', 'longest', etc.).
truncation: Whether to truncate sequences longer than `max_length`.
input_tokenizer: The internal tokenizer instance, loaded during initialization.
**Attributes**:
- **tokenizer_name** (`str | None`) -- The name of a pretrained tokenizer from the Hugging Face Hub
(e.g., "bert-base-uncased").
- **tokenizer** (`Any | None`) -- A pre-initialized tokenizer object. If provided, `tokenizer_name` is
ignored.
- **max_length** (`int`) -- The maximum length to pad or truncate sequences to.
- **task_key** (`str`) -- The key in `complementary_data` where the task string is stored.
- **padding_side** (`str`) -- The side to pad on ('left' or 'right').
- **padding** (`str`) -- The padding strategy ('max_length', 'longest', etc.).
- **truncation** (`bool`) -- Whether to truncate sequences longer than `max_length`.
- **input_tokenizer** (`Any`) -- The internal tokenizer instance, loaded during initialization.
"""
tokenizer_name: str | None = None
@@ -346,12 +348,17 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
Requires the `transformers` library to be installed.
Attributes:
tokenizer_name: The name of a pretrained processor from the Hugging Face Hub (e.g., "lerobot/fast-action-tokenizer").
tokenizer: A pre-initialized processor/tokenizer object. If provided, `tokenizer_name` is ignored.
trust_remote_code: Whether to trust remote code when loading the tokenizer (required for some tokenizers).
action_tokenizer: The internal tokenizer/processor instance, loaded during initialization.
paligemma_tokenizer_name: The name of a pretrained PaliGemma tokenizer from the Hugging Face Hub (e.g., "google/paligemma-3b-pt-224").
**Attributes**:
- **tokenizer_name** -- The name of a pretrained processor from the Hugging Face Hub (e.g.,
"lerobot/fast-action-tokenizer").
- **tokenizer** -- A pre-initialized processor/tokenizer object. If provided, `tokenizer_name` is
ignored.
- **trust_remote_code** (`bool`) -- Whether to trust remote code when loading the tokenizer (required
for some tokenizers).
- **action_tokenizer** (`Any`) -- The internal tokenizer/processor instance, loaded during
initialization.
- **paligemma_tokenizer_name** (`str`) -- The name of a pretrained PaliGemma tokenizer from the
Hugging Face Hub (e.g., "google/paligemma-3b-pt-224").
"""
action_tokenizer_name: str | None = None
@@ -38,11 +38,11 @@ class JointVelocityProcessorStep(ObservationProcessorStep):
difference between the current and the last observed joint positions. The
resulting velocity vector is then concatenated to the original state vector.
Attributes:
dt: The time step (delta time) in seconds between observations, used for
calculating velocity.
last_joint_positions: Stores the joint positions from the previous step
to enable velocity calculation.
**Attributes**:
- **dt** (`float`) -- The time step (delta time) in seconds between observations, used for calculating
velocity.
- **last_joint_positions** (`torch.Tensor | None`) -- Stores the joint positions from the previous
step to enable velocity calculation.
"""
dt: float = 0.1
@@ -138,9 +138,9 @@ class MotorCurrentProcessorStep(ObservationProcessorStep):
This step queries the robot's hardware interface to get the present current
for each motor and concatenates this information to the existing state vector.
Attributes:
robot: An instance of a `lerobot` Robot class that provides access to
the hardware bus.
**Attributes**:
- **robot** (`Robot | None`) -- An instance of a `lerobot` Robot class that provides access to the
hardware bus.
"""
robot: Robot | None = None
@@ -29,14 +29,18 @@ logger = logging.getLogger(__name__)
class BiOpenArmFollower(BimanualMixin, Robot):
"""
Bimanual OpenArm Follower Arms
"""
"""A bimanual pair of OpenArm follower arms driven as one robot."""
config_class = BiOpenArmFollowerConfig
name = "bi_openarm_follower"
def __init__(self, config: BiOpenArmFollowerConfig):
"""Build the robot from its configuration.
Args:
config (`BiOpenArmFollowerConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
super().__init__(config)
self.config = config
@@ -114,19 +118,43 @@ class BiOpenArmFollower(BimanualMixin, Robot):
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
"""The values this robot reports, and their types or shapes.
Returns:
`dict`: Keys as returned by [`~robots.Robot.get_observation`], mapped to a scalar type for
proprioceptive values or to a `(height, width, channels)` shape for images.
"""
return {**self._motors_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
"""The values this robot accepts, and their types.
Returns:
`dict`: Keys accepted by [`~robots.Robot.send_action`], mapped to their type.
"""
return self._motors_ft
def setup_motors(self) -> None:
"""Assign each motor its bus ID, one at a time.
Run this once when building the robot. Interactive: prompts you to connect the controller board to a
single motor at a time.
"""
raise NotImplementedError(
"Motor ID configuration is typically done via manufacturer tools for CAN motors."
)
@check_if_not_connected
def get_observation(self) -> RobotObservation:
"""Read the robot's current state and a frame from each camera.
Returns:
`dict[str, Any]`: Keys matching [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
obs_dict: RobotObservation = {}
# Add "left_" prefix to per-arm keys; keep top-level camera keys unprefixed.
@@ -146,6 +174,23 @@ class BiOpenArmFollower(BimanualMixin, Robot):
custom_kp: dict[str, float] | None = None,
custom_kd: dict[str, float] | None = None,
) -> RobotAction:
"""Command both arms to move towards a target configuration.
Args:
action (`dict[str, Any]`):
Target values, keyed as in [`~robots.Robot.action_features`], i.e. prefixed `left_` and
`right_`.
custom_kp (`dict[str, float]`, *optional*):
Per-motor proportional gains for this step only. Defaults to each arm's `position_kp`.
custom_kd (`dict[str, float]`, *optional*):
Per-motor derivative gains for this step only. Defaults to each arm's `position_kd`.
Returns:
`dict[str, Any]`: The action actually sent, which may be clipped by `max_relative_target`.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
# Remove "left_" prefix
left_action = {
key.removeprefix("left_"): value for key, value in action.items() if key.startswith("left_")
@@ -25,7 +25,28 @@ from ..openarm_follower import OpenArmFollowerConfigBase
@RobotConfig.register_subclass("bi_openarm_follower")
@dataclass(kw_only=True)
class BiOpenArmFollowerConfig(RobotConfig):
"""Configuration class for Bi OpenArm Follower robots."""
"""Configuration for a bimanual pair of OpenArm follower arms.
The two arms are configured independently, then driven as one robot: observation and action keys from
each arm are prefixed with `left_` and `right_`.
Calibration is per arm, taken from each arm config's own settings.
Args:
id (`str`, *optional*, defaults to `"bi_openarm_follower"`):
Identifier for the pair as a whole.
calibration_dir (`Path`, *optional*):
Unused at this level; each arm calibrates through its own config.
left_arm_config (`OpenArmFollowerConfigBase`):
Configuration for the left arm, including its own CAN interface. Set its `side` to `"left"` so
the correct joint limits apply.
right_arm_config (`OpenArmFollowerConfigBase`):
Configuration for the right arm, including its own CAN interface. Set its `side` to `"right"`.
cameras (`dict[str, CameraConfig]`, *optional*):
Cameras not attached to either arm, such as an overhead view. These keys appear in
observations unchanged, whereas cameras declared on an arm config are prefixed with that
arm's side.
"""
id: str | None = "bi_openarm_follower"
@@ -39,6 +39,12 @@ class BiRebotB601Follower(BimanualMixin, Robot):
name = "bi_rebot_b601_follower"
def __init__(self, config: BiRebotB601FollowerConfig):
"""Build the robot from its configuration.
Args:
config (`BiRebotB601FollowerConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
super().__init__(config)
self.config = config
@@ -120,14 +126,33 @@ class BiRebotB601Follower(BimanualMixin, Robot):
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
"""The values this robot reports, and their types or shapes.
Returns:
`dict`: Keys as returned by [`~robots.Robot.get_observation`], mapped to a scalar type for
proprioceptive values or to a `(height, width, channels)` shape for images.
"""
return {**self._motors_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
"""The values this robot accepts, and their types.
Returns:
`dict`: Keys accepted by [`~robots.Robot.send_action`], mapped to their type.
"""
return self._motors_ft
@check_if_not_connected
def get_observation(self) -> RobotObservation:
"""Read the robot's current state and a frame from each camera.
Returns:
`dict[str, Any]`: Keys matching [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
obs_dict: RobotObservation = {}
for k, v in self.left_arm.get_observation().items():
obs_dict[k if k in self._top_level_cam_keys else f"left_{k}"] = v
@@ -137,6 +162,18 @@ class BiRebotB601Follower(BimanualMixin, Robot):
@check_if_not_connected
def send_action(self, action: RobotAction) -> RobotAction:
"""Command the robot to move towards a target configuration.
Args:
action (`dict[str, Any]`):
Target values, keyed as in [`~robots.Robot.action_features`].
Returns:
`dict[str, Any]`: The action actually sent, which may be clipped by `max_relative_target`.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
left_action = {
key.removeprefix("left_"): value for key, value in action.items() if key.startswith("left_")
}
@@ -25,7 +25,27 @@ from ..rebot_b601_follower import RebotB601FollowerConfig
@RobotConfig.register_subclass("bi_rebot_b601_follower")
@dataclass
class BiRebotB601FollowerConfig(RobotConfig):
"""Configuration class for the bimanual reBot B601-DM follower robot."""
"""Configuration for a bimanual pair of reBot B601-DM follower arms.
The two arms are configured independently, then driven as one robot: observation and action keys from
each arm are prefixed with `left_` and `right_`.
Calibration is per arm, taken from each arm config's own `id` and `calibration_dir`.
Args:
left_arm_config (`RebotB601FollowerConfig`):
Configuration for the left arm, including its own `port` and CAN settings.
right_arm_config (`RebotB601FollowerConfig`):
Configuration for the right arm, including its own `port` and CAN settings.
cameras (`dict[str, CameraConfig]`, *optional*):
Cameras not attached to either arm, such as an overhead view. These keys appear in
observations unchanged, whereas cameras declared on an arm config are prefixed with that
arm's side.
id (`str`, *optional*):
Identifier for the pair as a whole.
calibration_dir (`Path`, *optional*):
Unused at this level; each arm calibrates through its own config.
"""
left_arm_config: RebotB601FollowerConfig
right_arm_config: RebotB601FollowerConfig
@@ -29,14 +29,18 @@ logger = logging.getLogger(__name__)
class BiSOFollower(BimanualMixin, Robot):
"""
[Bimanual SO Follower Arms](https://github.com/TheRobotStudio/SO-ARM100) designed by TheRobotStudio
"""
"""A bimanual pair of [SO follower arms](https://github.com/TheRobotStudio/SO-ARM100) by TheRobotStudio."""
config_class = BiSOFollowerConfig
name = "bi_so_follower"
def __init__(self, config: BiSOFollowerConfig):
"""Build the robot from its configuration.
Args:
config (`BiSOFollowerConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
super().__init__(config)
self.config = config
@@ -107,18 +111,42 @@ class BiSOFollower(BimanualMixin, Robot):
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
"""The values this robot reports, and their types or shapes.
Returns:
`dict`: Keys as returned by [`~robots.Robot.get_observation`], mapped to a scalar type for
proprioceptive values or to a `(height, width, channels)` shape for images.
"""
return {**self._motors_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
"""The values this robot accepts, and their types.
Returns:
`dict`: Keys accepted by [`~robots.Robot.send_action`], mapped to their type.
"""
return self._motors_ft
def setup_motors(self) -> None:
"""Assign each motor its bus ID, one at a time.
Run this once when building the robot. Interactive: prompts you to connect the controller board to a
single motor at a time.
"""
self.left_arm.setup_motors()
self.right_arm.setup_motors()
@check_if_not_connected
def get_observation(self) -> RobotObservation:
"""Read the robot's current state and a frame from each camera.
Returns:
`dict[str, Any]`: Keys matching [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
obs_dict: RobotObservation = {}
# Add "left_" prefix to per-arm keys; keep top-level camera keys unprefixed.
@@ -134,6 +162,18 @@ class BiSOFollower(BimanualMixin, Robot):
@check_if_not_connected
def send_action(self, action: RobotAction) -> RobotAction:
# Remove "left_" prefix
"""Command the robot to move towards a target configuration.
Args:
action (`dict[str, Any]`):
Target values, keyed as in [`~robots.Robot.action_features`].
Returns:
`dict[str, Any]`: The action actually sent, which may be clipped by `max_relative_target`.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
left_action = {
key.removeprefix("left_"): value for key, value in action.items() if key.startswith("left_")
}
@@ -25,7 +25,27 @@ from ..so_follower import SOFollowerConfig
@RobotConfig.register_subclass("bi_so_follower")
@dataclass
class BiSOFollowerConfig(RobotConfig):
"""Configuration class for Bi SO Follower robots."""
"""Configuration for a bimanual pair of SO follower arms.
The two arms are configured independently, then driven as one robot: observation and action keys from
each arm are prefixed with `left_` and `right_`.
Calibration is per arm, taken from each arm config's own `id` and `calibration_dir`.
Args:
left_arm_config (`SOFollowerConfig`):
Configuration for the left arm, including its own `port`.
right_arm_config (`SOFollowerConfig`):
Configuration for the right arm, including its own `port`.
cameras (`dict[str, CameraConfig]`, *optional*):
Cameras not attached to either arm, such as an overhead view. These keys appear in
observations unchanged, whereas cameras declared on an arm config are prefixed with that
arm's side.
id (`str`, *optional*):
Identifier for the pair as a whole.
calibration_dir (`Path`, *optional*):
Unused at this level; each arm calibrates through its own config.
"""
left_arm_config: SOFollowerConfig
right_arm_config: SOFollowerConfig
+27
View File
@@ -21,12 +21,33 @@ import draccus
@dataclass(kw_only=True)
class RobotConfig(draccus.ChoiceRegistry, abc.ABC):
"""Base configuration shared by every robot.
Concrete robots subclass this and register themselves with
`@RobotConfig.register_subclass("name")`, which is what makes `--robot.type=name` work on the command
line. Subclasses inherit the two fields below and must document them alongside their own.
Args:
id (`str`, *optional*):
Identifier for this particular unit, used to tell apart several robots of the same type. It
also names the calibration file, so keep it stable for a given piece of hardware.
calibration_dir (`Path`, *optional*):
Where to read and write the calibration file. Defaults to a per-robot directory under the
LeRobot calibration home.
"""
# Allows to distinguish between different robots of the same type
id: str | None = None
# Directory to store calibration file
calibration_dir: Path | None = None
def __post_init__(self):
"""Validate that every configured camera specifies the fields a robot requires.
Raises:
ValueError: If a camera does not set `width`, `height` and `fps`. A robot records frames at a
fixed shape, so these cannot be left to the driver's defaults.
"""
if hasattr(self, "cameras") and self.cameras:
for _, config in self.cameras.items():
for attr in ["width", "height", "fps"]:
@@ -37,4 +58,10 @@ class RobotConfig(draccus.ChoiceRegistry, abc.ABC):
@property
def type(self) -> str:
"""The registered name of this robot type.
Returns:
`str`: The name passed to `@RobotConfig.register_subclass`, e.g. `"so101_follower"`. This is
what `make_robot_from_config` dispatches on and what a user writes as `--robot.type=...`.
"""
return self.get_choice_name(self.__class__)
@@ -23,13 +23,18 @@ from ..config import RobotConfig
@RobotConfig.register_subclass("earthrover_mini_plus")
@dataclass
class EarthRoverMiniPlusConfig(RobotConfig):
"""Configuration for EarthRover Mini Plus robot using Frodobots SDK.
"""Configuration for the EarthRover Mini Plus rover.
This robot uses cloud-based control via the Frodobots SDK HTTP API.
Camera frames are accessed directly through SDK HTTP endpoints.
This robot is driven over the cloud through the Frodobots SDK's HTTP API rather than a local bus, so
there is no serial port and no LeRobot calibration file. Camera frames come from SDK HTTP endpoints.
Attributes:
sdk_url: URL of the Frodobots SDK server (default: http://localhost:8000)
Args:
sdk_url (`str`, *optional*, defaults to `"http://localhost:8000"`):
Base URL of the Frodobots SDK server. Commands and camera frames both go through it.
id (`str`, *optional*):
Identifier for this particular rover.
calibration_dir (`Path`, *optional*):
Unused: the rover exposes no calibration.
"""
sdk_url: str = "http://localhost:8000"
@@ -70,8 +70,7 @@ OBS_WHEEL_RPM_3 = "wheel_rpm_3"
class EarthRoverMiniPlus(Robot):
"""
EarthRover Mini Plus robot controlled via Frodobots SDK HTTP API.
"""EarthRover Mini Plus robot controlled via Frodobots SDK HTTP API.
This robot uses cloud-based control through the Frodobots SDK instead of direct
hardware connection. Cameras stream via WebRTC through Agora cloud, and control
@@ -82,9 +81,9 @@ class EarthRoverMiniPlus(Robot):
- Linear and angular velocity control
- Battery and orientation telemetry
Attributes:
config: Robot configuration
sdk_base_url: URL of the Frodobots SDK server (default: http://localhost:8000)
**Attributes**:
- **config** -- Robot configuration
- **sdk_base_url** -- URL of the Frodobots SDK server (default: http://localhost:8000)
"""
config_class = EarthRoverMiniPlusConfig
@@ -130,7 +129,6 @@ class EarthRoverMiniPlus(Robot):
DeviceAlreadyConnectedError: If robot is already connected
DeviceNotConnectedError: If cannot connect to SDK server
"""
# Verify SDK is running and accessible
try:
response = requests.get(f"{self.sdk_base_url}/data", timeout=10.0)
@@ -280,7 +278,6 @@ class EarthRoverMiniPlus(Robot):
Robot telemetry is retrieved from /data endpoint.
All SDK values are normalized to appropriate ranges for dataset recording.
"""
observation = {}
# Get camera images from SDK
@@ -370,7 +367,6 @@ class EarthRoverMiniPlus(Robot):
Raises:
DeviceNotConnectedError: If robot is not connected
"""
# Stop the robot before disconnecting
try:
self._send_command_to_sdk(0.0, 0.0)
@@ -24,6 +24,27 @@ from ..config import RobotConfig
@RobotConfig.register_subclass("hope_jr_hand")
@dataclass
class HopeJrHandConfig(RobotConfig):
"""Configuration for one Hope Jr hand.
Each hand is a separate robot, so a two-handed setup uses two of these with different `side` and
`port` values.
Args:
port (`str`):
Serial port the hand is connected to. Run `lerobot-find-port` to identify it.
side (`str`):
Which hand this is, `"left"` or `"right"`. Determines the motor layout, so it must match the
hardware.
disable_torque_on_disconnect (`bool`, *optional*, defaults to `True`):
Whether to release the motors on disconnect.
cameras (`dict[str, CameraConfig]`, *optional*):
Cameras to read alongside the hand's joint positions.
id (`str`, *optional*):
Identifier for this particular hand; also names its calibration file.
calibration_dir (`Path`, *optional*):
Where to read and write the calibration file. Defaults to the LeRobot calibration home.
"""
port: str # Port to connect to the hand
side: str # "left" / "right"
@@ -32,6 +53,12 @@ class HopeJrHandConfig(RobotConfig):
cameras: dict[str, CameraConfig] = field(default_factory=dict)
def __post_init__(self):
"""Validate the camera settings and the hand side.
Raises:
ValueError: If `side` is not `"left"` or `"right"`, or if a camera omits `width`, `height` or
`fps`.
"""
super().__post_init__()
if self.side not in ["right", "left"]:
raise ValueError(self.side)
@@ -40,6 +67,26 @@ class HopeJrHandConfig(RobotConfig):
@RobotConfig.register_subclass("hope_jr_arm")
@dataclass
class HopeJrArmConfig(RobotConfig):
"""Configuration for one Hope Jr arm.
Args:
port (`str`):
Serial port the arm is connected to. Run `lerobot-find-port` to identify it.
disable_torque_on_disconnect (`bool`, *optional*, defaults to `True`):
Whether to release the motors on disconnect. Leave `True` unless the arm is holding a load it
must not drop.
max_relative_target (`float | dict[str, float]`, *optional*):
Caps how far a single action may move the arm from its present position, as a safety limit. A
scalar applies to every motor; a dict maps motor name to a per-motor cap. `None` disables
clipping.
cameras (`dict[str, CameraConfig]`, *optional*):
Cameras to read alongside the arm's joint positions.
id (`str`, *optional*):
Identifier for this particular arm; also names its calibration file.
calibration_dir (`Path`, *optional*):
Where to read and write the calibration file. Defaults to the LeRobot calibration home.
"""
port: str # Port to connect to the hand
disable_torque_on_disconnect: bool = True
+80 -4
View File
@@ -35,10 +35,22 @@ logger = logging.getLogger(__name__)
class HopeJrArm(Robot):
"""One arm of the Hope Jr humanoid.
The arm and the hand are separate robots; pair this with [`~robots.hope_jr.HopeJrHand`] for a full
limb. See [`~robots.Robot`] for the contract every method here implements.
"""
config_class = HopeJrArmConfig
name = "hope_jr_arm"
def __init__(self, config: HopeJrArmConfig):
"""Build the robot from its configuration.
Args:
config (`HopeJrArmConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
super().__init__(config)
self.config = config
self.bus = FeetechMotorsBus(
@@ -77,23 +89,47 @@ class HopeJrArm(Robot):
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
"""The values this robot reports, and their types or shapes.
Returns:
`dict`: Keys as returned by [`~robots.Robot.get_observation`], mapped to a scalar type for
proprioceptive values or to a `(height, width, channels)` shape for images.
"""
return {**self._motors_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
"""The values this robot accepts, and their types.
Returns:
`dict`: Keys accepted by [`~robots.Robot.send_action`], mapped to their type.
"""
return self._motors_ft
@property
def is_connected(self) -> bool:
"""Whether every device this robot uses is connected.
Returns:
`bool`: `True` only when the robot and all its cameras are connected.
"""
return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
"""
We assume that at connection time, arm is in a rest position,
and torque can be safely disabled to run calibration.
"""
"""Connect the motor bus and cameras, calibrating and configuring the arm.
> [!WARNING]
> The arm is assumed to be at rest when this is called, because torque is disabled to run
> calibration.
Args:
calibrate (`bool`, *optional*, defaults to `True`):
Whether to run calibration if the arm is not already calibrated.
Raises:
DeviceAlreadyConnectedError: If the robot is already connected.
"""
self.bus.connect(handshake=False)
if not self.is_calibrated and calibrate:
self.calibrate()
@@ -107,9 +143,18 @@ class HopeJrArm(Robot):
@property
def is_calibrated(self) -> bool:
"""Whether the robot is calibrated.
Returns:
`bool`: `True` when no calibration is needed before use.
"""
return self.bus.is_calibrated
def calibrate(self) -> None:
"""Calibrate the robot and store the result.
Interactive: prompts on stdin and asks you to move the robot through the required positions.
"""
groups = {
"all": list(self.bus.motors.keys()),
"shoulder": ["shoulder_pitch", "shoulder_yaw", "shoulder_roll"],
@@ -122,11 +167,17 @@ class HopeJrArm(Robot):
print("Calibration saved to", self.calibration_fpath)
def configure(self) -> None:
"""Apply the operating mode, gains and limits from the configuration to the robot."""
with self.bus.torque_disabled():
self.bus.configure_motors(maximum_acceleration=30, acceleration=30)
def setup_motors(self) -> None:
# TODO: add docstring
"""Assign each motor its bus ID, one at a time.
Run this once when building the robot. Interactive: prompts you to connect the controller board to a
single motor at a time.
"""
for motor in reversed(self.bus.motors):
input(f"Connect the controller board to the '{motor}' motor only and press enter.")
self.bus.setup_motor(motor)
@@ -135,6 +186,14 @@ class HopeJrArm(Robot):
@check_if_not_connected
def get_observation(self) -> RobotObservation:
# Read arm position
"""Read the robot's current state and a frame from each camera.
Returns:
`dict[str, Any]`: Keys matching [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
start = time.perf_counter()
obs_dict = self.bus.sync_read("Present_Position", self.other_motors)
obs_dict[self.shoulder_pitch] = self.bus.read("Present_Position", self.shoulder_pitch)
@@ -160,6 +219,18 @@ class HopeJrArm(Robot):
@check_if_not_connected
def send_action(self, action: RobotAction) -> RobotAction:
"""Command the robot to move towards a target configuration.
Args:
action (`dict[str, Any]`):
Target values, keyed as in [`~robots.Robot.action_features`].
Returns:
`dict[str, Any]`: The action actually sent, which may be clipped by `max_relative_target`.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
# Cap goal position when too far away from present position.
@@ -174,6 +245,11 @@ class HopeJrArm(Robot):
@check_if_not_connected
def disconnect(self):
"""Disconnect from the robot and its cameras.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
self.bus.disconnect(self.config.disable_torque_on_disconnect)
for cam in self.cameras.values():
cam.disconnect()
@@ -59,10 +59,22 @@ LEFT_HAND_INVERSIONS = [
class HopeJrHand(Robot):
"""One hand of the Hope Jr humanoid.
Each hand is its own robot, so a two-handed setup uses two of these with different `side` values. See
[`~robots.Robot`] for the contract every method here implements.
"""
config_class = HopeJrHandConfig
name = "hope_jr_hand"
def __init__(self, config: HopeJrHandConfig):
"""Build the robot from its configuration.
Args:
config (`HopeJrHandConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
super().__init__(config)
self.config = config
self.bus = FeetechMotorsBus(
@@ -113,18 +125,43 @@ class HopeJrHand(Robot):
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
"""The values this robot reports, and their types or shapes.
Returns:
`dict`: Keys as returned by [`~robots.Robot.get_observation`], mapped to a scalar type for
proprioceptive values or to a `(height, width, channels)` shape for images.
"""
return {**self._motors_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
"""The values this robot accepts, and their types.
Returns:
`dict`: Keys accepted by [`~robots.Robot.send_action`], mapped to their type.
"""
return self._motors_ft
@property
def is_connected(self) -> bool:
"""Whether every device this robot uses is connected.
Returns:
`bool`: `True` only when the robot and all its cameras are connected.
"""
return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
"""Connect to the robot and its cameras, then apply the configured settings.
Args:
calibrate (`bool`, *optional*, defaults to `True`):
Whether to run calibration if the robot is not already calibrated.
Raises:
DeviceAlreadyConnectedError: If the robot is already connected.
"""
self.bus.connect()
if not self.is_calibrated and calibrate:
self.calibrate()
@@ -138,9 +175,18 @@ class HopeJrHand(Robot):
@property
def is_calibrated(self) -> bool:
"""Whether the robot is calibrated.
Returns:
`bool`: `True` when no calibration is needed before use.
"""
return self.bus.is_calibrated
def calibrate(self) -> None:
"""Calibrate the robot and store the result.
Interactive: prompts on stdin and asks you to move the robot through the required positions.
"""
fingers = {}
for finger in ["thumb", "index", "middle", "ring", "pinky"]:
fingers[finger] = [motor for motor in self.bus.motors if motor.startswith(finger)]
@@ -152,11 +198,17 @@ class HopeJrHand(Robot):
print("Calibration saved to", self.calibration_fpath)
def configure(self) -> None:
"""Apply the operating mode, gains and limits from the configuration to the robot."""
with self.bus.torque_disabled():
self.bus.configure_motors()
def setup_motors(self) -> None:
# TODO: add docstring
"""Assign each motor its bus ID, one at a time.
Run this once when building the robot. Interactive: prompts you to connect the controller board to a
single motor at a time.
"""
for motor in self.bus.motors:
input(f"Connect the controller board to the '{motor}' motor only and press enter.")
self.bus.setup_motor(motor)
@@ -164,6 +216,14 @@ class HopeJrHand(Robot):
@check_if_not_connected
def get_observation(self) -> RobotObservation:
"""Read the robot's current state and a frame from each camera.
Returns:
`dict[str, Any]`: Keys matching [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
obs_dict = {}
# Read hand position
@@ -191,12 +251,29 @@ class HopeJrHand(Robot):
@check_if_not_connected
def send_action(self, action: RobotAction) -> RobotAction:
"""Command the robot to move towards a target configuration.
Args:
action (`dict[str, Any]`):
Target values, keyed as in [`~robots.Robot.action_features`].
Returns:
`dict[str, Any]`: The action actually sent, which may be clipped by `max_relative_target`.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
self.bus.sync_write("Goal_Position", goal_pos)
return action
@check_if_not_connected
def disconnect(self):
"""Disconnect from the robot and its cameras.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
self.bus.disconnect(self.config.disable_torque_on_disconnect)
for cam in self.cameras.values():
cam.disconnect()
@@ -22,6 +22,30 @@ from ..config import RobotConfig
@RobotConfig.register_subclass("koch_follower")
@dataclass
class KochFollowerConfig(RobotConfig):
"""Configuration for the Koch v1.1 follower arm.
Args:
port (`str`):
Serial port the arm is connected to, e.g. `/dev/ttyACM0` on Linux or `COM3` on Windows. Run
`lerobot-find-port` to identify it.
disable_torque_on_disconnect (`bool`, *optional*, defaults to `True`):
Whether to release the motors on disconnect. Leave `True` unless the arm is holding a load it
must not drop.
max_relative_target (`float | dict[str, float]`, *optional*):
Caps how far a single action may move the arm from its present position, as a safety limit. A
scalar applies to every motor; a dict maps motor name to a per-motor cap. `None` disables
clipping.
cameras (`dict[str, CameraConfig]`, *optional*):
Cameras to read alongside the arm's joint positions, keyed by the name they appear under in
observations. Each must specify `width`, `height` and `fps`.
use_degrees (`bool`, *optional*, defaults to `False`):
Whether to report and accept joint positions in degrees rather than as a normalised range.
id (`str`, *optional*):
Identifier for this particular arm; also names its calibration file.
calibration_dir (`Path`, *optional*):
Where to read and write the calibration file. Defaults to the LeRobot calibration home.
"""
# Port to connect to the arm
port: str
@@ -35,16 +35,24 @@ logger = logging.getLogger(__name__)
class KochFollower(Robot):
"""
- [Koch v1.0](https://github.com/AlexanderKoch-Koch/low_cost_robot), with and without the wrist-to-elbow
expansion, developed by Alexander Koch from [Tau Robotics](https://tau-robotics.com)
- [Koch v1.1](https://github.com/jess-moss/koch-v1-1) developed by Jess Moss
"""The Koch follower arm, in either of its two revisions.
- [Koch v1.0](https://github.com/AlexanderKoch-Koch/low_cost_robot), with and without the
wrist-to-elbow expansion, developed by Alexander Koch from
[Tau Robotics](https://tau-robotics.com).
- [Koch v1.1](https://github.com/jess-moss/koch-v1-1), developed by Jess Moss.
"""
config_class = KochFollowerConfig
name = "koch_follower"
def __init__(self, config: KochFollowerConfig):
"""Build the robot from its configuration.
Args:
config (`KochFollowerConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
super().__init__(config)
self.config = config
norm_mode_body = MotorNormMode.DEGREES if config.use_degrees else MotorNormMode.RANGE_M100_100
@@ -79,23 +87,47 @@ class KochFollower(Robot):
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
"""The values this robot reports, and their types or shapes.
Returns:
`dict`: Keys as returned by [`~robots.Robot.get_observation`], mapped to a scalar type for
proprioceptive values or to a `(height, width, channels)` shape for images.
"""
return {**self._motors_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
"""The values this robot accepts, and their types.
Returns:
`dict`: Keys accepted by [`~robots.Robot.send_action`], mapped to their type.
"""
return self._motors_ft
@property
def is_connected(self) -> bool:
"""Whether every device this robot uses is connected.
Returns:
`bool`: `True` only when the robot and all its cameras are connected.
"""
return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
"""
We assume that at connection time, arm is in a rest position,
and torque can be safely disabled to run calibration.
"""
"""Connect the motor bus and cameras, calibrating and configuring the arm.
> [!WARNING]
> The arm is assumed to be at rest when this is called, because torque is disabled to run
> calibration.
Args:
calibrate (`bool`, *optional*, defaults to `True`):
Whether to run calibration if the arm is not already calibrated.
Raises:
DeviceAlreadyConnectedError: If the robot is already connected.
"""
self.bus.connect()
if not self.is_calibrated and calibrate:
logger.info(
@@ -111,9 +143,18 @@ class KochFollower(Robot):
@property
def is_calibrated(self) -> bool:
"""Whether the robot is calibrated.
Returns:
`bool`: `True` when no calibration is needed before use.
"""
return self.bus.is_calibrated
def calibrate(self) -> None:
"""Calibrate the robot and store the result.
Interactive: prompts on stdin and asks you to move the robot through the required positions.
"""
self.bus.disable_torque()
if self.calibration:
# Calibration file exists, ask user whether to use it or run new calibration
@@ -157,6 +198,7 @@ class KochFollower(Robot):
logger.info(f"Calibration saved to {self.calibration_fpath}")
def configure(self) -> None:
"""Apply the operating mode, gains and limits from the configuration to the robot."""
with self.bus.torque_disabled():
self.bus.configure_motors()
# Use 'extended position mode' for all motors except gripper, because in joint mode the servos
@@ -181,6 +223,11 @@ class KochFollower(Robot):
self.bus.write("Position_D_Gain", "elbow_flex", 600)
def setup_motors(self) -> None:
"""Assign each motor its bus ID, one at a time.
Run this once when building the robot. Interactive: prompts you to connect the controller board to a
single motor at a time.
"""
for motor in reversed(self.bus.motors):
input(f"Connect the controller board to the '{motor}' motor only and press enter.")
self.bus.setup_motor(motor)
@@ -189,6 +236,14 @@ class KochFollower(Robot):
@check_if_not_connected
def get_observation(self) -> RobotObservation:
# Read arm position
"""Read the robot's current state and a frame from each camera.
Returns:
`dict[str, Any]`: Keys matching [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
start = time.perf_counter()
obs_dict = self.bus.sync_read("Present_Position")
obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
@@ -225,7 +280,6 @@ class KochFollower(Robot):
Returns:
RobotAction: The action sent to the motors, potentially clipped.
"""
goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
# Cap goal position when too far away from present position.
@@ -241,6 +295,11 @@ class KochFollower(Robot):
@check_if_not_connected
def disconnect(self):
"""Disconnect from the robot and its cameras.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
self.bus.disconnect(self.config.disable_torque_on_disconnect)
for cam in self.cameras.values():
cam.disconnect()
@@ -21,6 +21,12 @@ from ..config import RobotConfig
def lekiwi_cameras_config() -> dict[str, CameraConfig]:
"""Build the default camera set for a LeKiwi base.
Returns:
`dict[str, CameraConfig]`: The `front` and `wrist` OpenCV cameras at the device paths and
rotations of a standard LeKiwi build. Override the `cameras` field if yours is wired differently.
"""
return {
"front": OpenCVCameraConfig(
index_or_path="/dev/video0",
@@ -44,6 +50,34 @@ def lekiwi_cameras_config() -> dict[str, CameraConfig]:
@RobotConfig.register_subclass("lekiwi")
@dataclass
class LeKiwiConfig(RobotConfig):
"""Configuration for LeKiwi, running on the robot itself.
This is the config used by the process on the LeKiwi's own computer. To drive one from another machine,
use [`LeKiwiClientConfig`] instead.
Args:
port (`str`, *optional*, defaults to `"/dev/ttyACM0"`):
Serial port of the motor bus on the robot's computer.
disable_torque_on_disconnect (`bool`, *optional*, defaults to `True`):
Whether to release the motors on disconnect.
max_relative_target (`float | dict[str, float]`, *optional*):
Caps how far a single action may move the arm from its present position, as a safety limit. A
scalar applies to every motor; a dict maps motor name to a per-motor cap. `None` disables
clipping.
cameras (`dict[str, CameraConfig]`, *optional*):
Cameras to read alongside the joint positions. Defaults to the standard `front` and `wrist`
build; see [`lekiwi_cameras_config`].
use_degrees (`bool`, *optional*, defaults to `True`):
Whether to report and accept arm joint positions in degrees.
num_read_retries (`int`, *optional*, defaults to 2):
Extra attempts when a `sync_read` fails. Feetech buses occasionally return a corrupted status
packet, which would otherwise abort the control loop.
id (`str`, *optional*):
Identifier for this particular robot; also names its calibration file.
calibration_dir (`Path`, *optional*):
Where to read and write the calibration file. Defaults to the LeRobot calibration home.
"""
port: str = "/dev/ttyACM0" # port to connect to the bus
disable_torque_on_disconnect: bool = True
@@ -67,6 +101,22 @@ class LeKiwiConfig(RobotConfig):
@dataclass
class LeKiwiHostConfig:
"""Configuration for the host process that serves a LeKiwi over the network.
Args:
port_zmq_cmd (`int`, *optional*, defaults to 5555):
ZMQ port the host listens on for actions.
port_zmq_observations (`int`, *optional*, defaults to 5556):
ZMQ port the host publishes observations on.
connection_time_s (`int`, *optional*, defaults to 30):
How long the host stays up before shutting down.
watchdog_timeout_ms (`int`, *optional*, defaults to 500):
Stop the robot if no command arrives within this window. Guards against a dropped client
leaving the base driving.
max_loop_freq_hz (`int`, *optional*, defaults to 30):
Control loop frequency. Lower it if the robot jitters, and watch CPU load with `top`.
"""
# Network Configuration
port_zmq_cmd: int = 5555
port_zmq_observations: int = 5556
@@ -84,6 +134,32 @@ class LeKiwiHostConfig:
@RobotConfig.register_subclass("lekiwi_client")
@dataclass
class LeKiwiClientConfig(RobotConfig):
"""Configuration for driving a LeKiwi from another machine.
Presents the same [`~robots.Robot`] interface as the robot-side [`LeKiwiConfig`], but every call goes
over ZMQ to the host process. Calibration lives on the robot, so nothing here configures it.
Args:
remote_ip (`str`):
IP address of the LeKiwi's computer on the network.
port_zmq_cmd (`int`, *optional*, defaults to 5555):
ZMQ port to send actions to. Must match the host's `port_zmq_cmd`.
port_zmq_observations (`int`, *optional*, defaults to 5556):
ZMQ port to receive observations on. Must match the host's `port_zmq_observations`.
teleop_keys (`dict[str, str]`, *optional*):
Keyboard bindings for driving the base: movement, rotation, speed control and quit.
cameras (`dict[str, CameraConfig]`, *optional*):
Cameras expected in the observation stream. Defaults to the standard `front` and `wrist` build.
polling_timeout_ms (`int`, *optional*, defaults to 15):
How long to wait for an observation before giving up on that step.
connect_timeout_s (`int`, *optional*, defaults to 5):
How long to wait for the host to answer when connecting.
id (`str`, *optional*):
Identifier for this particular robot.
calibration_dir (`Path`, *optional*):
Unused by the client: calibration is held on the robot.
"""
# Network Configuration
remote_ip: str
port_zmq_cmd: int = 5555
+69 -11
View File
@@ -39,17 +39,25 @@ logger = logging.getLogger(__name__)
class LeKiwi(Robot):
"""
The robot includes a three omniwheel mobile base and a remote follower arm.
The leader arm is connected locally (on the laptop) and its joint positions are recorded and then
forwarded to the remote follower arm (after applying a safety clamp).
In parallel, keyboard teleoperation is used to generate raw velocity commands for the wheels.
"""A three-omniwheel mobile base with a follower arm on top, running on the robot itself.
The leader arm is connected to the operator's laptop; its joint positions are recorded and forwarded
to this follower arm after a safety clamp. In parallel, keyboard teleoperation generates raw velocity
commands for the wheels.
To drive one of these from another machine, use [`~robots.lekiwi.LeKiwiClient`].
"""
config_class = LeKiwiConfig
name = "lekiwi"
def __init__(self, config: LeKiwiConfig):
"""Build the robot from its configuration.
Args:
config (`LeKiwiConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
super().__init__(config)
self.config = config
norm_mode_body = MotorNormMode.DEGREES if config.use_degrees else MotorNormMode.RANGE_M100_100
@@ -105,18 +113,43 @@ class LeKiwi(Robot):
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
"""The values this robot reports, and their types or shapes.
Returns:
`dict`: Keys as returned by [`~robots.Robot.get_observation`], mapped to a scalar type for
proprioceptive values or to a `(height, width, channels)` shape for images.
"""
return {**self._state_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
"""The values this robot accepts, and their types.
Returns:
`dict`: Keys accepted by [`~robots.Robot.send_action`], mapped to their type.
"""
return self._state_ft
@property
def is_connected(self) -> bool:
"""Whether every device this robot uses is connected.
Returns:
`bool`: `True` only when the robot and all its cameras are connected.
"""
return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
"""Connect to the robot and its cameras, then apply the configured settings.
Args:
calibrate (`bool`, *optional*, defaults to `True`):
Whether to run calibration if the robot is not already calibrated.
Raises:
DeviceAlreadyConnectedError: If the robot is already connected.
"""
self.bus.connect()
if not self.is_calibrated and calibrate:
logger.info(
@@ -132,9 +165,18 @@ class LeKiwi(Robot):
@property
def is_calibrated(self) -> bool:
"""Whether the robot is calibrated.
Returns:
`bool`: `True` when no calibration is needed before use.
"""
return self.bus.is_calibrated
def calibrate(self) -> None:
"""Calibrate the robot and store the result.
Interactive: prompts on stdin and asks you to move the robot through the required positions.
"""
if self.calibration:
# Calibration file exists, ask user whether to use it or run new calibration
user_input = input(
@@ -189,6 +231,7 @@ class LeKiwi(Robot):
# Set-up arm actuators (position mode)
# We assume that at connection time, arm is in a rest position,
# and torque can be safely disabled to run calibration.
"""Apply the operating mode, gains and limits from the configuration to the robot."""
self.bus.disable_torque()
self.bus.configure_motors()
for name in self.arm_motors:
@@ -205,6 +248,11 @@ class LeKiwi(Robot):
self.bus.enable_torque()
def setup_motors(self) -> None:
"""Assign each motor its bus ID, one at a time.
Run this once when building the robot. Interactive: prompts you to connect the controller board to a
single motor at a time.
"""
for motor in chain(reversed(self.arm_motors), reversed(self.base_motors)):
input(f"Connect the controller board to the '{motor}' motor only and press enter.")
self.bus.setup_motor(motor)
@@ -238,8 +286,7 @@ class LeKiwi(Robot):
base_radius: float = 0.125,
max_raw: int = 3000,
) -> dict:
"""
Convert desired body-frame velocities into wheel raw commands.
"""Convert desired body-frame velocities into wheel raw commands.
Parameters:
x_cmd : Linear velocity in x (m/s).
@@ -302,8 +349,7 @@ class LeKiwi(Robot):
wheel_radius: float = 0.05,
base_radius: float = 0.125,
) -> dict[str, Any]:
"""
Convert wheel raw command feedback back into body-frame velocities.
"""Convert wheel raw command feedback back into body-frame velocities.
Parameters:
wheel_raw : Vector with raw wheel commands ("base_left_wheel", "base_back_wheel", "base_right_wheel").
@@ -313,7 +359,6 @@ class LeKiwi(Robot):
Returns:
A dict (x.vel, y.vel, theta.vel) all in m/s
"""
# Convert each raw command back to an angular speed in deg/s.
wheel_degps = np.array(
[
@@ -346,6 +391,14 @@ class LeKiwi(Robot):
@check_if_not_connected
def get_observation(self) -> RobotObservation:
# Read actuators position for arm and vel for base
"""Read the robot's current state and a frame from each camera.
Returns:
`dict[str, Any]`: Keys matching [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
start = time.perf_counter()
arm_pos = self.bus.sync_read(
"Present_Position", self.arm_motors, num_retry=self.config.num_read_retries
@@ -390,7 +443,6 @@ class LeKiwi(Robot):
Returns:
RobotAction: the action sent to the motors, potentially clipped.
"""
arm_goal_pos = {k: v for k, v in action.items() if k.endswith(".pos")}
base_goal_vel = {k: v for k, v in action.items() if k.endswith(".vel")}
@@ -419,11 +471,17 @@ class LeKiwi(Robot):
return {**arm_goal_pos, **base_goal_vel}
def stop_base(self):
"""Bring the mobile base to a halt by commanding zero velocity on its wheels."""
self.bus.sync_write("Goal_Velocity", dict.fromkeys(self.base_motors, 0), num_retry=5)
logger.info("Base motors stopped")
@check_if_not_connected
def disconnect(self):
"""Disconnect from the robot and its cameras.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
self.stop_base()
self.bus.disconnect(self.config.disable_torque_on_disconnect)
for cam in self.cameras.values():
+65 -14
View File
@@ -31,10 +31,23 @@ from .config_lekiwi import LeKiwiClientConfig
class LeKiwiClient(Robot):
"""Drives a LeKiwi over the network from another machine.
Presents the same [`~robots.Robot`] interface as [`~robots.lekiwi.LeKiwi`], but every observation and
action crosses a ZMQ connection to the host process running on the robot. Calibration stays on the
robot, so this class does not perform it.
"""
config_class = LeKiwiClientConfig
name = "lekiwi_client"
def __init__(self, config: LeKiwiClientConfig):
"""Build the robot from its configuration.
Args:
config (`LeKiwiClientConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
import zmq
self._zmq = zmq
@@ -105,24 +118,50 @@ class LeKiwiClient(Robot):
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
"""The values this robot reports, and their types or shapes.
Returns:
`dict`: Keys as returned by [`~robots.Robot.get_observation`], mapped to a scalar type for
proprioceptive values or to a `(height, width, channels)` shape for images.
"""
return {**self._state_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
"""The values this robot accepts, and their types.
Returns:
`dict`: Keys accepted by [`~robots.Robot.send_action`], mapped to their type.
"""
return self._state_ft
@property
def is_connected(self) -> bool:
"""Whether every device this robot uses is connected.
Returns:
`bool`: `True` only when the robot and all its cameras are connected.
"""
return self._is_connected
@property
def is_calibrated(self) -> bool:
"""Whether the robot is calibrated.
Returns:
`bool`: `True` when no calibration is needed before use.
"""
pass
@check_if_already_connected
def connect(self) -> None:
"""Establishes ZMQ sockets with the remote mobile robot"""
"""Open the ZMQ command and observation sockets to the LeKiwi host.
Takes no `calibrate` argument: calibration lives on the robot, not on the client.
Raises:
DeviceAlreadyConnectedError: If the client is already connected.
"""
zmq = self._zmq
self.zmq_context = zmq.Context()
self.zmq_cmd_socket = self.zmq_context.socket(zmq.PUSH)
@@ -146,6 +185,10 @@ class LeKiwiClient(Robot):
self._is_connected = True
def calibrate(self) -> None:
"""Calibrate the robot and store the result.
Interactive: prompts on stdin and asks you to move the robot through the required positions.
"""
pass
def _poll_and_get_latest_message(self) -> list[bytes] | None:
@@ -203,7 +246,6 @@ class LeKiwiClient(Robot):
self, observation: RobotObservation
) -> tuple[dict[str, np.ndarray], RobotObservation]:
"""Extracts frames, and state from the parsed observation."""
flat_state = {key: observation.get(key, 0.0) for key in self._state_order}
state_vec = np.array([flat_state[key] for key in self._state_order], dtype=np.float32)
@@ -222,14 +264,12 @@ class LeKiwiClient(Robot):
return current_frames, obs_dict
def _get_data(self) -> tuple[dict[str, np.ndarray], RobotObservation]:
"""
Polls the video socket for the latest observation data.
"""Polls the video socket for the latest observation data.
Attempts to retrieve and decode the latest message within a short timeout.
If successful, updates and returns the new frames, speed, and arm state.
If no new data arrives or decoding fails, returns the last known values.
"""
# 1. Get the latest message's frames from the socket
latest_frames = self._poll_and_get_latest_message()
@@ -258,12 +298,17 @@ class LeKiwiClient(Robot):
@check_if_not_connected
def get_observation(self) -> RobotObservation:
"""
Capture observations from the remote robot: current follower arm positions,
present wheel speeds (converted to body-frame velocities: x, y, theta),
and a camera frame. Receives over ZMQ, translate to body-frame vel
"""
"""Receive one observation from the remote robot over ZMQ.
Wheel speeds arrive as raw motor velocities and are converted here to body-frame `x`, `y` and
`theta`.
Returns:
`dict[str, Any]`: Follower arm positions, body-frame base velocities and camera frames.
Raises:
DeviceNotConnectedError: If the client is not connected.
"""
frames, obs_dict = self._get_data()
# Loop over each configured camera
@@ -308,21 +353,24 @@ class LeKiwiClient(Robot):
}
def configure(self):
"""Apply the operating mode, gains and limits from the configuration to the robot."""
pass
@check_if_not_connected
def send_action(self, action: RobotAction) -> RobotAction:
"""Command lekiwi to move to a target joint configuration. Translates to motor space + sends over ZMQ
"""Send a target configuration to the remote robot over ZMQ.
Body-frame base velocities are translated into wheel velocities before sending.
Args:
action (RobotAction): array containing the goal positions for the motors.
action (`dict[str, Any]`): Goal positions for the arm and body-frame velocities for the base.
Raises:
RobotDeviceNotConnectedError: if robot is not connected.
Returns:
np.ndarray: the action sent to the motors, potentially clipped.
"""
# Action values may be torch tensors (e.g. replayed from a dataset) or numpy
# scalars; json.dumps only serializes Python primitives, so coerce each value to a
# plain float before sending.
@@ -338,8 +386,11 @@ class LeKiwiClient(Robot):
@check_if_not_connected
def disconnect(self):
"""Cleans ZMQ comms"""
"""Close the ZMQ sockets and terminate the context.
Raises:
DeviceNotConnectedError: If the client is not connected.
"""
self.zmq_observation_socket.close()
self.zmq_cmd_socket.close()
self.zmq_context.term()
+23
View File
@@ -36,7 +36,19 @@ class LeKiwiServerConfig:
class LeKiwiHost:
"""Serves a [`~robots.lekiwi.LeKiwi`] over ZMQ so a client can drive it from another machine.
Runs on the robot's own computer, receiving actions on one socket and publishing observations on
another.
"""
def __init__(self, config: LeKiwiHostConfig):
"""Bind the command and observation sockets.
Args:
config (`LeKiwiHostConfig`):
Ports, loop frequency and watchdog settings for the host.
"""
self.zmq_context = zmq.Context()
self.zmq_cmd_socket = self.zmq_context.socket(zmq.PULL)
self.zmq_cmd_socket.setsockopt(zmq.CONFLATE, 1)
@@ -53,6 +65,11 @@ class LeKiwiHost:
self.max_loop_freq_hz = config.max_loop_freq_hz
def disconnect(self):
"""Disconnect from the robot and its cameras.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
self.zmq_observation_socket.close()
self.zmq_cmd_socket.close()
self.zmq_context.term()
@@ -60,6 +77,12 @@ class LeKiwiHost:
@draccus.wrap()
def main(cfg: LeKiwiServerConfig):
"""Run the LeKiwi host loop until the configured connection time elapses.
Args:
cfg (`LeKiwiServerConfig`):
The robot and host configuration to serve.
"""
logging.info("Configuring LeKiwi")
robot = LeKiwi(cfg.robot)
@@ -22,6 +22,30 @@ from ..config import RobotConfig
@RobotConfig.register_subclass("omx_follower")
@dataclass
class OmxFollowerConfig(RobotConfig):
"""Configuration for the OpenMANIPULATOR-X follower arm.
Args:
port (`str`):
Serial port the arm is connected to, e.g. `/dev/ttyUSB0`. Run `lerobot-find-port` to identify
it.
disable_torque_on_disconnect (`bool`, *optional*, defaults to `True`):
Whether to release the motors on disconnect. Leave `True` unless the arm is holding a load it
must not drop.
max_relative_target (`float | dict[str, float]`, *optional*):
Caps how far a single action may move the arm from its present position, as a safety limit. A
scalar applies to every motor; a dict maps motor name to a per-motor cap. `None` disables
clipping.
cameras (`dict[str, CameraConfig]`, *optional*):
Cameras to read alongside the arm's joint positions, keyed by the name they appear under in
observations. Each must specify `width`, `height` and `fps`.
use_degrees (`bool`, *optional*, defaults to `False`):
Whether to report and accept joint positions in degrees rather than as a normalised range.
id (`str`, *optional*):
Identifier for this particular arm; also names its calibration file.
calibration_dir (`Path`, *optional*):
Where to read and write the calibration file. Defaults to the LeRobot calibration home.
"""
# Port to connect to the arm
port: str
+68 -10
View File
@@ -36,15 +36,21 @@ logger = logging.getLogger(__name__)
class OmxFollower(Robot):
"""
- [OMX](https://github.com/ROBOTIS-GIT/open_manipulator),
expansion, developed by Woojin Wie and Junha Cha from [ROBOTIS](https://ai.robotis.com/)
"""The [OpenMANIPULATOR-X](https://github.com/ROBOTIS-GIT/open_manipulator) follower arm.
Developed by Woojin Wie and Junha Cha at [ROBOTIS](https://ai.robotis.com/).
"""
config_class = OmxFollowerConfig
name = "omx_follower"
def __init__(self, config: OmxFollowerConfig):
"""Build the robot from its configuration.
Args:
config (`OmxFollowerConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
super().__init__(config)
self.config = config
norm_mode_body = MotorNormMode.DEGREES if config.use_degrees else MotorNormMode.RANGE_M100_100
@@ -79,25 +85,50 @@ class OmxFollower(Robot):
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
"""The values this robot reports, and their types or shapes.
Returns:
`dict`: Keys as returned by [`~robots.Robot.get_observation`], mapped to a scalar type for
proprioceptive values or to a `(height, width, channels)` shape for images.
"""
return {**self._motors_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
"""The values this robot accepts, and their types.
Returns:
`dict`: Keys accepted by [`~robots.Robot.send_action`], mapped to their type.
"""
return self._motors_ft
@property
def is_connected(self) -> bool:
"""Whether every device this robot uses is connected.
Returns:
`bool`: `True` only when the robot and all its cameras are connected.
"""
return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
"""
For OMX robots that come pre-calibrated:
- If default calibration from package doesn't match motors, read from motors and save
- This allows using pre-calibrated robots without manual calibration
- If no calibration file exists, use factory default values (homing_offset=0, range_min=0, range_max=4095)
"""
"""Connect the motor bus and cameras, handling the pre-calibrated case.
OMX arms ship calibrated, so this avoids asking for a manual calibration where possible:
- if the packaged default calibration does not match the motors, the motors' own values are read
and saved;
- if no calibration file exists, factory defaults are used (`homing_offset=0`, `range_min=0`,
`range_max=4095`).
Args:
calibrate (`bool`, *optional*, defaults to `True`):
Whether to calibrate if the arm is not already calibrated.
Raises:
DeviceAlreadyConnectedError: If the robot is already connected.
"""
self.bus.connect()
if not self.is_calibrated and calibrate:
logger.info(
@@ -113,9 +144,18 @@ class OmxFollower(Robot):
@property
def is_calibrated(self) -> bool:
"""Whether the robot is calibrated.
Returns:
`bool`: `True` when no calibration is needed before use.
"""
return self.bus.is_calibrated
def calibrate(self) -> None:
"""Calibrate the robot and store the result.
Interactive: prompts on stdin and asks you to move the robot through the required positions.
"""
self.bus.disable_torque()
logger.info(f"\nUsing factory default calibration values for {self}")
logger.info(f"\nWriting default configuration of {self} to the motors")
@@ -140,6 +180,7 @@ class OmxFollower(Robot):
logger.info(f"Calibration saved to {self.calibration_fpath}")
def configure(self) -> None:
"""Apply the operating mode, gains and limits from the configuration to the robot."""
with self.bus.torque_disabled():
self.bus.configure_motors()
# Use 'extended position mode' for all motors except gripper, because in joint mode the servos
@@ -164,6 +205,11 @@ class OmxFollower(Robot):
self.bus.write("Position_D_Gain", "elbow_flex", 600)
def setup_motors(self) -> None:
"""Assign each motor its bus ID, one at a time.
Run this once when building the robot. Interactive: prompts you to connect the controller board to a
single motor at a time.
"""
for motor in reversed(self.bus.motors):
input(f"Connect the controller board to the '{motor}' motor only and press enter.")
self.bus.setup_motor(motor)
@@ -172,6 +218,14 @@ class OmxFollower(Robot):
@check_if_not_connected
def get_observation(self) -> RobotObservation:
# Read arm position
"""Read the robot's current state and a frame from each camera.
Returns:
`dict[str, Any]`: Keys matching [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
start = time.perf_counter()
obs_dict = self.bus.sync_read("Present_Position")
obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
@@ -208,7 +262,6 @@ class OmxFollower(Robot):
Returns:
RobotAction: The action sent to the motors, potentially clipped.
"""
goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
# Cap goal position when too far away from present position.
@@ -224,6 +277,11 @@ class OmxFollower(Robot):
@check_if_not_connected
def disconnect(self):
"""Disconnect from the robot and its cameras.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
self.bus.disconnect(self.config.disable_torque_on_disconnect)
for cam in self.cameras.values():
cam.disconnect()
@@ -45,7 +45,13 @@ RIGHT_DEFAULT_JOINTS_LIMITS: dict[str, tuple[float, float]] = {
@dataclass
class OpenArmFollowerConfigBase:
"""Base configuration for the OpenArms follower robot with Damiao motors."""
"""Field definitions for the OpenArm follower, a 7-DOF arm plus gripper on Damiao CAN motors.
This class only carries the fields. The registered configuration users instantiate is
[`OpenArmFollowerConfig`], which documents them all in one place doc-builder renders only a class's
own docstring, never its bases'. It is also used directly as the per-arm config of
[`~robots.bi_openarm_follower.BiOpenArmFollowerConfig`].
"""
# CAN interfaces - one per arm
# arm CAN interface (e.g., "can1")
@@ -123,4 +129,57 @@ class OpenArmFollowerConfigBase:
@RobotConfig.register_subclass("openarm_follower")
@dataclass
class OpenArmFollowerConfig(RobotConfig, OpenArmFollowerConfigBase):
"""Configuration for a single OpenArm follower arm.
OpenArm is a 7-DOF arm plus gripper on Damiao CAN motors, so `port` names a CAN interface rather than a
serial device. Calibration follows the usual LeRobot flow and is stored per `id`.
> [!WARNING]
> `joint_limits` defaults to a deliberately tiny range so an uncalibrated arm cannot swing. Set `side`
> to `"left"` or `"right"` to get the real limits for that arm, or pass your own.
The per-joint lists `position_kp`, `position_kd` hold 8 values in motor order: `joint_1` through
`joint_7`, then `gripper`.
Args:
port (`str`):
CAN interface the arm is on, e.g. `"can0"` on Linux.
side (`str`, *optional*):
Which arm this is, `"left"` or `"right"`. Selects that side's joint limits. Leaving it `None`
keeps the small safety defaults.
can_interface (`str`, *optional*, defaults to `"socketcan"`):
CAN backend: `"socketcan"` on Linux, `"slcan"` for a serial adapter, or `"auto"` to detect.
use_can_fd (`bool`, *optional*, defaults to `True`):
Whether to use CAN FD. OpenArm uses it by default.
can_bitrate (`int`, *optional*, defaults to 1000000):
Nominal CAN bitrate, 1 Mbps.
can_data_bitrate (`int`, *optional*, defaults to 5000000):
CAN FD data bitrate, 5 Mbps. Only used when `use_can_fd` is `True`.
disable_torque_on_disconnect (`bool`, *optional*, defaults to `True`):
Whether to release the motors on disconnect. Leave `True` unless the arm is holding a load it
must not drop.
use_velocity_and_torque (`bool`, *optional*, defaults to `False`):
Whether to expose `.vel` and `.torque` per motor in the observation features. Kept `False` by
default for compatibility with the position-only `openarm_mini` teleoperator.
max_relative_target (`float | dict[str, float]`, *optional*):
Caps how far a single action may move the arm from its present position, as a safety limit. A
scalar applies to every motor; a dict maps motor name to a per-motor cap. `None` disables
clipping.
cameras (`dict[str, CameraConfig]`, *optional*):
Cameras to read alongside the arm's joint positions.
motor_config (`dict[str, tuple[int, int, str]]`, *optional*):
Maps motor name to `(send_can_id, recv_can_id, motor_type)`. Defaults to the stock OpenArm
layout; change it only if you have rewired or re-addressed the motors.
position_kp (`list[float]`, *optional*):
MIT-mode proportional gains used by `send_action`, 8 values in motor order.
position_kd (`list[float]`, *optional*):
MIT-mode derivative gains used by `send_action`, 8 values in motor order.
joint_limits (`dict[str, tuple[float, float]]`, *optional*):
Soft `(min, max)` limits in degrees per joint, clipped against on every action.
id (`str`, *optional*):
Identifier for this particular arm; also names its calibration file.
calibration_dir (`Path`, *optional*):
Where to read and write the calibration file. Defaults to the LeRobot calibration home.
"""
pass
@@ -37,15 +37,22 @@ logger = logging.getLogger(__name__)
class OpenArmFollower(Robot):
"""
OpenArms Follower Robot which uses CAN bus communication to control 7 DOF arm with a gripper.
The arm uses Damiao motors in MIT control mode.
"""The OpenArm follower: a 7-DOF arm plus gripper on a CAN bus.
Uses Damiao motors in MIT control mode. See [`~robots.Robot`] for the contract every method here
implements.
"""
config_class = OpenArmFollowerConfig
name = "openarm_follower"
def __init__(self, config: OpenArmFollowerConfig):
"""Build the robot from its configuration.
Args:
config (`OpenArmFollowerConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
super().__init__(config)
self.config = config
@@ -127,13 +134,11 @@ class OpenArmFollower(Robot):
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
"""
Connect to the robot and optionally calibrate.
"""Connect to the robot and optionally calibrate.
We assume that at connection time, the arms are in a safe rest position,
and torque can be safely disabled to run calibration if needed.
"""
# Connect to CAN bus
logger.info(f"Connecting arm on {self.config.port}...")
self.bus.connect()
@@ -160,8 +165,7 @@ class OpenArmFollower(Robot):
return self.bus.is_calibrated
def calibrate(self) -> None:
"""
Run calibration procedure for OpenArms robot.
"""Run calibration procedure for OpenArms robot.
The calibration procedure:
1. Disable torque
@@ -217,14 +221,18 @@ class OpenArmFollower(Robot):
self.bus.configure_motors()
def setup_motors(self) -> None:
"""Assign each motor its bus ID, one at a time.
Run this once when building the robot. Interactive: prompts you to connect the controller board to a
single motor at a time.
"""
raise NotImplementedError(
"Motor ID configuration is typically done via manufacturer tools for CAN motors."
)
@check_if_not_connected
def get_observation(self) -> RobotObservation:
"""
Get current observation from robot including position, velocity, and torque.
"""Get current observation from robot including position, velocity, and torque.
Reads all motor states (pos/vel/torque) in one CAN refresh cycle
instead of 3 separate reads.
@@ -268,8 +276,7 @@ class OpenArmFollower(Robot):
custom_kp: dict[str, float] | None = None,
custom_kd: dict[str, float] | None = None,
) -> RobotAction:
"""
Send action command to robot.
"""Send action command to robot.
The action magnitude may be clipped based on safety limits.
@@ -281,7 +288,6 @@ class OpenArmFollower(Robot):
Returns:
The action actually sent (potentially clipped)
"""
goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
# Apply joint limit clipping to arm
@@ -343,7 +349,6 @@ class OpenArmFollower(Robot):
@check_if_not_connected
def disconnect(self):
"""Disconnect from robot."""
# Disconnect CAN bus
self.bus.disconnect(self.config.disable_torque_on_disconnect)
@@ -23,6 +23,58 @@ from ..config import RobotConfig
@RobotConfig.register_subclass("reachy2")
@dataclass
class Reachy2RobotConfig(RobotConfig):
"""Configuration for the Reachy 2 humanoid.
Reachy 2 is driven over the network rather than a serial bus, so `port` is a TCP port on the robot's
gRPC service rather than a device path. Calibration is handled by the robot itself and there is no
LeRobot calibration file.
Which joints appear in observations and actions is selected by the `with_*` flags: turning a part off
removes its joints entirely. At least one part must stay enabled.
Args:
max_relative_target (`float`, *optional*):
Caps how far a single action may move a joint from its present position, as a safety limit.
`None` disables clipping.
ip_address (`str`, *optional*, defaults to `"localhost"`):
Address of the Reachy 2 robot.
port (`int`, *optional*, defaults to 50065):
TCP port of the robot's service. Not a serial port.
disable_torque_on_disconnect (`bool`, *optional*, defaults to `False`):
Whether to call `turn_off_smoothly()` before disconnecting.
use_external_commands (`bool`, *optional*, defaults to `False`):
Set `True` when another system drives the robot, such as the official
[teleoperation app](https://github.com/pollen-robotics/Reachy2Teleoperation). In that mode
[`~robots.Robot.send_action`] does not send anything to the robot.
with_mobile_base (`bool`, *optional*, defaults to `True`):
Whether to include the mobile base's joints.
with_l_arm (`bool`, *optional*, defaults to `True`):
Whether to include the left arm's joints.
with_r_arm (`bool`, *optional*, defaults to `True`):
Whether to include the right arm's joints.
with_neck (`bool`, *optional*, defaults to `True`):
Whether to include the neck's joints.
with_antennas (`bool`, *optional*, defaults to `True`):
Whether to include the antennas' joints.
with_left_teleop_camera (`bool`, *optional*, defaults to `False`):
Whether to add the left teleoperation camera to observations.
with_right_teleop_camera (`bool`, *optional*, defaults to `False`):
Whether to add the right teleoperation camera to observations.
with_torso_camera (`bool`, *optional*, defaults to `False`):
Whether to add the torso RGB camera to observations.
camera_width (`int`, *optional*, defaults to 640):
Frame width for the built-in cameras. Their frame rate is fixed at 30 and is not configurable.
camera_height (`int`, *optional*, defaults to 480):
Frame height for the built-in cameras.
cameras (`dict[str, CameraConfig]`, *optional*):
Additional cameras beyond the three built-in ones. The `with_*_camera` flags populate this
field, so anything set here is merged with them.
id (`str`, *optional*):
Identifier for this particular robot.
calibration_dir (`Path`, *optional*):
Unused: Reachy 2 manages its own calibration.
"""
# `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
# Set this to a positive scalar to have the same value for all motors.
max_relative_target: float | None = None
@@ -65,6 +117,11 @@ class Reachy2RobotConfig(RobotConfig):
cameras: dict[str, CameraConfig] = field(default_factory=dict)
def __post_init__(self) -> None:
"""Add the built-in cameras selected by the `with_*_camera` flags and validate the part selection.
Raises:
ValueError: If every robot part is disabled, which would leave no joints to control.
"""
# Add cameras with same ip_address as the robot
if self.with_left_teleop_camera:
self.cameras["teleop_left"] = Reachy2CameraConfig(
+79 -3
View File
@@ -73,14 +73,18 @@ REACHY2_VEL = {
class Reachy2Robot(Robot):
"""
[Reachy 2](https://www.pollen-robotics.com/reachy/), by Pollen Robotics.
"""
"""[Reachy 2](https://www.pollen-robotics.com/reachy/), the humanoid by Pollen Robotics."""
config_class = Reachy2RobotConfig
name = "reachy2"
def __init__(self, config: Reachy2RobotConfig):
"""Build the robot from its configuration.
Args:
config (`Reachy2RobotConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
require_package("reachy2_sdk", extra="reachy2")
super().__init__(config)
@@ -97,18 +101,41 @@ class Reachy2Robot(Robot):
@property
def observation_features(self) -> dict[str, Any]:
"""The values this robot reports, and their types or shapes.
Returns:
`dict`: Keys as returned by [`~robots.Robot.get_observation`], mapped to a scalar type for
proprioceptive values or to a `(height, width, channels)` shape for images.
"""
return {**self.motors_features, **self.camera_features}
@property
def action_features(self) -> dict[str, type]:
"""The values this robot accepts, and their types.
Returns:
`dict`: Keys accepted by [`~robots.Robot.send_action`], mapped to their type.
"""
return self.motors_features
@property
def camera_features(self) -> dict[str, tuple[int | None, int | None, int]]:
"""The shape of each configured camera's frames.
Returns:
`dict[str, tuple[int | None, int | None, int]]`: Camera name mapped to
`(height, width, channels)`.
"""
return {cam: (self.cameras[cam].height, self.cameras[cam].width, 3) for cam in self.cameras}
@property
def motors_features(self) -> dict[str, type]:
"""The joints this robot exposes, given which parts are enabled in the config.
Returns:
`dict[str, type]`: Joint name mapped to `float`, including the mobile base's velocity
components when `with_mobile_base` is set.
"""
if self.config.with_mobile_base:
return {
**dict.fromkeys(
@@ -125,9 +152,23 @@ class Reachy2Robot(Robot):
@property
def is_connected(self) -> bool:
"""Whether every device this robot uses is connected.
Returns:
`bool`: `True` only when the robot and all its cameras are connected.
"""
return self.reachy.is_connected() if self.reachy is not None else False
def connect(self, calibrate: bool = False) -> None:
"""Connect to the robot and its cameras, then apply the configured settings.
Args:
calibrate (`bool`, *optional*, defaults to `False`):
Accepted for interface compatibility and ignored: Reachy 2 manages its own calibration.
Raises:
DeviceAlreadyConnectedError: If the robot is already connected.
"""
self.reachy = ReachySDK(self.config.ip_address)
if not self.is_connected:
raise ConnectionError()
@@ -138,15 +179,25 @@ class Reachy2Robot(Robot):
self.configure()
def configure(self) -> None:
"""Apply the operating mode, gains and limits from the configuration to the robot."""
if self.reachy is not None:
self.reachy.turn_on()
self.reachy.reset_default_limits()
@property
def is_calibrated(self) -> bool:
"""Whether the robot is calibrated.
Returns:
`bool`: `True` when no calibration is needed before use.
"""
return True
def calibrate(self) -> None:
"""Calibrate the robot and store the result.
Interactive: prompts on stdin and asks you to move the robot through the required positions.
"""
pass
def _generate_joints_dict(self) -> dict[str, str]:
@@ -172,6 +223,14 @@ class Reachy2Robot(Robot):
return {}
def get_observation(self) -> RobotObservation:
"""Read the robot's current state and a frame from each camera.
Returns:
`dict[str, Any]`: Keys matching [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
obs_dict: RobotObservation = {}
# Read Reachy 2 state
@@ -186,6 +245,18 @@ class Reachy2Robot(Robot):
return obs_dict
def send_action(self, action: RobotAction) -> RobotAction:
"""Command the robot to move towards a target configuration.
Args:
action (`dict[str, Any]`):
Target values, keyed as in [`~robots.Robot.action_features`].
Returns:
`dict[str, Any]`: The action actually sent, which may be clipped by `max_relative_target`.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
if self.reachy is not None:
if not self.is_connected:
raise ConnectionError()
@@ -228,6 +299,11 @@ class Reachy2Robot(Robot):
return action
def disconnect(self) -> None:
"""Disconnect from the robot and its cameras.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
if self.reachy is not None:
for cam in self.cameras.values():
cam.disconnect()
@@ -23,10 +23,12 @@ from ..config import RobotConfig
@dataclass
class RebotB601FollowerConfig:
"""Base configuration class for the Seeed Studio reBot B601-DM follower arm.
"""Field definitions for the Seeed Studio reBot B601-DM follower arm.
The B601-DM is a 6-DOF arm plus gripper driven by Damiao CAN motors. Motor
communication goes through the ``motorbridge`` package.
This class only carries the fields. The registered configuration users instantiate is
[`RebotB601FollowerRobotConfig`], which documents them all in one place doc-builder renders only a
class's own docstring, never its bases'. It is also used directly as the per-arm config of
[`~robots.bi_rebot_b601_follower.BiRebotB601FollowerConfig`].
"""
# Communication port. For ``can_adapter="damiao"`` this is the Damiao serial
@@ -104,6 +106,62 @@ class RebotB601FollowerConfig:
@RobotConfig.register_subclass("rebot_b601_follower")
@dataclass
class RebotB601FollowerRobotConfig(RobotConfig, RebotB601FollowerConfig):
"""Registered configuration for the reBot B601-DM follower robot."""
"""Configuration for the Seeed Studio reBot B601-DM follower arm.
The B601-DM is a 6-DOF arm plus gripper on Damiao CAN motors, driven through the `motorbridge`
package. What `port` means depends on `can_adapter`. Calibration follows the usual LeRobot flow and is
stored per `id`.
The arm and the gripper are controlled separately: `control_mode` governs the six arm joints and
`gripper_control_mode` the gripper, and each mode uses a different subset of the gain fields.
Per-joint lists hold 7 values in motor order: `shoulder_pan`, `shoulder_lift`, `elbow_flex`,
`wrist_flex`, `wrist_yaw`, `wrist_roll`, `gripper`.
Args:
port (`str`):
Where the arm is reached. For `can_adapter="damiao"` this is the serial bridge device, e.g.
`/dev/ttyACM0`; for `can_adapter="socketcan"` it is the CAN channel name, e.g. `can0`.
can_adapter (`str`, *optional*, defaults to `"damiao"`):
`"damiao"` for the dedicated Damiao serial bridge, or `"socketcan"` for SocketCAN adapters
such as PCAN, slcan and embedded controllers.
dm_serial_baud (`int`, *optional*, defaults to 921600):
Baud rate of the Damiao serial bridge. Only used when `can_adapter="damiao"`.
disable_torque_on_disconnect (`bool`, *optional*, defaults to `True`):
Whether to release the motors on disconnect. Leave `True` unless the arm is holding a load it
must not drop.
max_relative_target (`float | dict[str, float]`, *optional*):
Caps how far a single action may move the arm from its present position, in degrees. A scalar
applies to every motor; a dict maps motor name to a per-motor cap. `None` disables clipping.
cameras (`dict[str, CameraConfig]`, *optional*):
Cameras to read alongside the arm's joint positions.
motor_can_ids (`dict[str, tuple[int, int]]`, *optional*):
Maps motor name to its `(send_can_id, recv_can_id)` pair. Change it only if you have
re-addressed the motors.
pos_vel_velocity (`float | list[float]`, *optional*):
Maximum speed in deg/s per joint, used by the arm in `pos_vel` mode and by the gripper in
`force_pos` mode.
control_mode (`str`, *optional*, defaults to `"mit"`):
How the six arm joints are driven: `"mit"` or `"pos_vel"`.
mit_kp (`float | list[float]`, *optional*):
MIT-mode proportional gains per arm joint. Unused when `control_mode="pos_vel"`.
mit_kd (`float | list[float]`, *optional*):
MIT-mode derivative gains per arm joint. Unused when `control_mode="pos_vel"`.
gripper_control_mode (`str`, *optional*, defaults to `"force_pos"`):
How the gripper is driven: `"force_pos"` or `"mit"`.
gripper_torque_ratio (`float`, *optional*, defaults to 0.07):
Maximum grip force as a fraction in `[0, 1]`. Only used when
`gripper_control_mode="force_pos"`.
gripper_mit_kp (`float`, *optional*, defaults to 8.0):
Gripper MIT-mode proportional gain. Only used when `gripper_control_mode="mit"`.
gripper_mit_kd (`float`, *optional*, defaults to 0.3):
Gripper MIT-mode derivative gain. Only used when `gripper_control_mode="mit"`.
joint_limits (`dict[str, tuple[float, float]]`, *optional*):
Soft `(min, max)` limits in degrees per joint, clipped against on every action.
id (`str`, *optional*):
Identifier for this particular arm; also names its calibration file.
calibration_dir (`Path`, *optional*):
Where to read and write the calibration file. Defaults to the LeRobot calibration home.
"""
pass
@@ -66,6 +66,12 @@ class RebotB601Follower(Robot):
name = "rebot_b601_follower"
def __init__(self, config: RebotB601FollowerRobotConfig):
"""Build the robot from its configuration.
Args:
config (`RebotB601FollowerRobotConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
require_package("motorbridge", extra="rebot")
super().__init__(config)
self.config = config
@@ -91,18 +97,43 @@ class RebotB601Follower(Robot):
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
"""The values this robot reports, and their types or shapes.
Returns:
`dict`: Keys as returned by [`~robots.Robot.get_observation`], mapped to a scalar type for
proprioceptive values or to a `(height, width, channels)` shape for images.
"""
return {**self._motors_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
"""The values this robot accepts, and their types.
Returns:
`dict`: Keys accepted by [`~robots.Robot.send_action`], mapped to their type.
"""
return self._motors_ft
@property
def is_connected(self) -> bool:
"""Whether every device this robot uses is connected.
Returns:
`bool`: `True` only when the robot and all its cameras are connected.
"""
return self.bus is not None and all(cam.is_connected for cam in self.cameras.values())
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
"""Connect to the robot and its cameras, then apply the configured settings.
Args:
calibrate (`bool`, *optional*, defaults to `True`):
Whether to run calibration if the robot is not already calibrated.
Raises:
DeviceAlreadyConnectedError: If the robot is already connected.
"""
logger.info(f"Connecting {self} on {self.config.port} (adapter={self.config.can_adapter})...")
if self.config.can_adapter == "damiao":
self.bus = MotorBridgeController.from_dm_serial(
@@ -133,9 +164,18 @@ class RebotB601Follower(Robot):
@property
def is_calibrated(self) -> bool:
"""Whether the robot is calibrated.
Returns:
`bool`: `True` when no calibration is needed before use.
"""
return bool(self.calibration)
def calibrate(self) -> None:
"""Calibrate the robot and store the result.
Interactive: prompts on stdin and asks you to move the robot through the required positions.
"""
if self.calibration:
user_input = input(
f"Press ENTER to use provided calibration file associated with the id {self.id}, "
@@ -174,6 +214,7 @@ class RebotB601Follower(Robot):
print(f"Calibration saved to {self.calibration_fpath}")
def configure(self) -> None:
"""Apply the operating mode, gains and limits from the configuration to the robot."""
if self.config.control_mode not in ("pos_vel", "mit"):
raise ValueError(
f"Unsupported control_mode '{self.config.control_mode}'. Use 'pos_vel' or 'mit'."
@@ -226,6 +267,14 @@ class RebotB601Follower(Robot):
@check_if_not_connected
def get_observation(self) -> RobotObservation:
"""Read the robot's current state and a frame from each camera.
Returns:
`dict[str, Any]`: Keys matching [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
start = time.perf_counter()
obs_dict = {f"{motor}.pos": pos for motor, pos in self._present_pos().items()}
dt_ms = (time.perf_counter() - start) * 1e3
@@ -311,6 +360,11 @@ class RebotB601Follower(Robot):
@check_if_not_connected
def disconnect(self) -> None:
"""Disconnect from the robot and its cameras.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
for motor in self.motors.values():
if self.config.disable_torque_on_disconnect:
motor.disable()
+95 -63
View File
@@ -28,15 +28,22 @@ from .config import RobotConfig
# TODO(aliberts): action/obs typing such as Generic[ObsType, ActType] similar to gym.Env ?
# https://github.com/Farama-Foundation/Gymnasium/blob/3287c869f9a48d99454306b0d4b4ec537f0f35e3/gymnasium/core.py#L23
class Robot(abc.ABC):
"""
The base abstract class for all LeRobot-compatible robots.
"""The base abstract class for all LeRobot-compatible robots.
This class provides a standardized interface for interacting with physical robots.
Subclasses must implement all abstract methods and properties to be usable.
This class provides a standardized interface for interacting with physical robots. Subclasses must
implement all abstract methods and properties to be usable.
Attributes:
config_class (RobotConfig): The expected configuration class for this robot.
name (str): The unique robot name used to identify this robot type.
Used as a context manager, a robot connects on entry and disconnects on exit even if the body raises:
```python
>>> with SO101Follower(config) as robot: # doctest: +SKIP
... obs = robot.get_observation()
... robot.send_action(action)
```
**Attributes**:
- **config_class** (`type[RobotConfig]`) -- The expected configuration class for this robot.
- **name** (`str`) -- The unique robot name used to identify this robot type.
"""
# Set these in ALL subclasses
@@ -44,6 +51,13 @@ class Robot(abc.ABC):
name: str
def __init__(self, config: RobotConfig):
"""Set up identity and calibration paths, loading an existing calibration file if there is one.
Args:
config (`RobotConfig`):
The robot's configuration. Its `id` and `calibration_dir` decide where calibration is
read from and written to.
"""
self.robot_type = self.name
self.id = config.id
self.calibration_dir = (
@@ -56,28 +70,24 @@ class Robot(abc.ABC):
self._load_calibration()
def __str__(self) -> str:
"""Return this robot's id and class name, e.g. `"my_arm SO101Follower"`.
Returns:
`str`: A short identifier used in log messages.
"""
return f"{self.id} {self.__class__.__name__}"
def __enter__(self):
"""
Context manager entry.
Automatically connects to the camera.
"""
"""Context manager entry. Automatically connects to the robot."""
self.connect()
return self
def __exit__(self, exc_type, exc_value, traceback) -> None:
"""
Context manager exit.
Automatically disconnects, ensuring resources are released even on error.
"""
"""Context manager exit. Disconnects, ensuring resources are released even on error."""
self.disconnect()
def __del__(self) -> None:
"""
Destructor safety net.
Attempts to disconnect if the object is garbage collected without cleanup.
"""
"""Destructor safety net. Disconnects if the object is garbage collected without cleanup."""
try:
if self.is_connected:
self.disconnect()
@@ -88,83 +98,102 @@ class Robot(abc.ABC):
@property
@abc.abstractmethod
def observation_features(self) -> dict:
"""
A dictionary describing the structure and types of the observations produced by the robot.
Its structure (keys) should match the structure of what is returned by :pymeth:`get_observation`.
Values for the dict should either be:
- The type of the value if it's a simple value, e.g. `float` for single proprioceptive value (a joint's position/velocity)
- A tuple representing the shape if it's an array-type value, e.g. `(height, width, channel)` for images
"""A dictionary describing the structure and types of the observations produced by the robot.
Note: this property should be able to be called regardless of whether the robot is connected or not.
Its keys should match the structure of what is returned by [`~robots.Robot.get_observation`]. Values
should either be:
- the type of the value if it's a simple value, e.g. `float` for a single proprioceptive value
(a joint's position or velocity)
- a tuple representing the shape if it's an array-type value, e.g. `(height, width, channel)` for
images
> [!NOTE]
> This property must be callable regardless of whether the robot is connected.
Returns:
`dict`: Observation names mapped to their type or shape.
"""
pass
@property
@abc.abstractmethod
def action_features(self) -> dict:
"""
A dictionary describing the structure and types of the actions expected by the robot. Its structure
(keys) should match the structure of what is passed to :pymeth:`send_action`. Values for the dict
should be the type of the value if it's a simple value, e.g. `float` for single proprioceptive value
(a joint's goal position/velocity)
"""A dictionary describing the structure and types of the actions expected by the robot.
Note: this property should be able to be called regardless of whether the robot is connected or not.
Its keys should match the structure of what is passed to [`~robots.Robot.send_action`]. Values should
be the type of the value if it's a simple value, e.g. `float` for a single proprioceptive value
(a joint's goal position or velocity).
> [!NOTE]
> This property must be callable regardless of whether the robot is connected.
Returns:
`dict`: Action names mapped to their type or shape.
"""
pass
@property
@abc.abstractmethod
def is_connected(self) -> bool:
"""
Whether the robot is currently connected or not. If `False`, calling :pymeth:`get_observation` or
:pymeth:`send_action` should raise an error.
"""Whether the robot is currently connected.
If `False`, calling [`~robots.Robot.get_observation`] or [`~robots.Robot.send_action`] should raise
an error.
Returns:
`bool`: `True` if communication with the robot is established.
"""
pass
@abc.abstractmethod
def connect(self, calibrate: bool = True) -> None:
"""
Establish communication with the robot.
"""Establish communication with the robot.
Args:
calibrate (bool): If True, automatically calibrate the robot after connecting if it's not
calibrated or needs calibration (this is hardware-dependant).
calibrate (`bool`, *optional*, defaults to `True`):
Whether to automatically calibrate the robot after connecting, if it is not calibrated or
needs recalibration. Whether calibration is needed is hardware-dependent.
"""
pass
@property
@abc.abstractmethod
def is_calibrated(self) -> bool:
"""Whether the robot is currently calibrated or not. Should be always `True` if not applicable"""
"""Whether the robot is currently calibrated.
Returns:
`bool`: `True` if the robot is calibrated. Always `True` for robots where calibration does not
apply.
"""
pass
@abc.abstractmethod
def calibrate(self) -> None:
"""
Calibrate the robot if applicable. If not, this should be a no-op.
"""Calibrate the robot if applicable. If not, this should be a no-op.
This method should collect any necessary data (e.g., motor offsets) and update the
:pyattr:`calibration` dictionary accordingly.
This method should collect any necessary data (e.g. motor offsets) and update the `calibration`
dictionary accordingly.
"""
pass
def _load_calibration(self, fpath: Path | None = None) -> None:
"""
Helper to load calibration data from the specified file.
"""Helper to load calibration data from the specified file.
Args:
fpath (Path | None): Optional path to the calibration file. Defaults to `self.calibration_fpath`.
fpath (`Path`, *optional*):
Path to the calibration file. Defaults to `self.calibration_fpath`.
"""
fpath = self.calibration_fpath if fpath is None else fpath
with open(fpath) as f, draccus.config_type("json"):
self.calibration = draccus.load(dict[str, MotorCalibration], f)
def _save_calibration(self, fpath: Path | None = None) -> None:
"""
Helper to save calibration data to the specified file.
"""Helper to save calibration data to the specified file.
Args:
fpath (Path | None): Optional path to save the calibration file. Defaults to `self.calibration_fpath`.
fpath (`Path`, *optional*):
Path to save the calibration file to. Defaults to `self.calibration_fpath`.
"""
fpath = self.calibration_fpath if fpath is None else fpath
with open(fpath, "w") as f, draccus.config_type("json"):
@@ -172,36 +201,39 @@ class Robot(abc.ABC):
@abc.abstractmethod
def configure(self) -> None:
"""
Apply any one-time or runtime configuration to the robot.
"""Apply any one-time or runtime configuration to the robot.
This may include setting motor parameters, control modes, or initial state.
"""
pass
@abc.abstractmethod
def get_observation(self) -> RobotObservation:
"""
Retrieve the current observation from the robot.
"""Retrieve the current observation from the robot.
Returns:
RobotObservation: A flat dictionary representing the robot's current sensory state. Its structure
should match :pymeth:`observation_features`.
"""
`dict[str, Any]`: A flat dictionary representing the robot's current sensory state. Its structure
should match [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If [`~robots.Robot.connect`] has not been called.
"""
pass
@abc.abstractmethod
def send_action(self, action: RobotAction) -> RobotAction:
"""
Send an action command to the robot.
"""Send an action command to the robot.
Args:
action (RobotAction): Dictionary representing the desired action. Its structure should match
:pymeth:`action_features`.
action (`dict[str, Any]`):
The desired action. Its structure should match [`~robots.Robot.action_features`].
Returns:
RobotAction: The action actually sent to the motors potentially clipped or modified, e.g. by
safety limits on velocity.
`dict[str, Any]`: The action actually sent to the motors, potentially clipped or modified, e.g.
by safety limits on velocity. Prefer this over the requested action when logging or recording.
Raises:
DeviceNotConnectedError: If [`~robots.Robot.connect`] has not been called.
"""
pass
@@ -23,7 +23,12 @@ from ..config import RobotConfig
@dataclass
class SOFollowerConfig:
"""Base configuration class for SO Follower robots."""
"""Field definitions shared by the SO-family follower arms.
This class only carries the fields. The registered configuration users instantiate is
[`SOFollowerRobotConfig`], which combines these with [`~robots.RobotConfig`] and documents them all in
one place doc-builder renders only a class's own docstring, never its bases'.
"""
# Port to connect to the arm
port: str
@@ -57,6 +62,51 @@ class SOFollowerConfig:
@RobotConfig.register_subclass("so100_follower")
@dataclass
class SOFollowerRobotConfig(RobotConfig, SOFollowerConfig):
"""Configuration for the SO-100 and SO-101 follower arms.
Both arms share this class; `SO100FollowerConfig` and `SO101FollowerConfig` are aliases for it. They
differ in their calibration and gearing, not in their control code.
Args:
port (`str`):
Serial port the arm is connected to, e.g. `/dev/ttyACM0` on Linux or `COM3` on Windows. Run
`lerobot-find-port` to identify it.
disable_torque_on_disconnect (`bool`, *optional*, defaults to `True`):
Whether to release the motors on disconnect. Leave `True` unless the arm is holding a load it
must not drop.
max_relative_target (`float | dict[str, float]`, *optional*):
Caps how far a single action may move the arm from its present position, as a safety limit. A
scalar applies to every motor; a dict maps motor name to a per-motor cap. `None` disables
clipping. Enabling this costs an extra read of the present position on every step.
cameras (`dict[str, CameraConfig]`, *optional*):
Cameras to read alongside the arm's joint positions, keyed by the name they appear under in
observations. Each must specify `width`, `height` and `fps`.
use_degrees (`bool`, *optional*, defaults to `True`):
Whether to report and accept joint positions in degrees. Keep `True` for compatibility with
existing policies and datasets.
position_p_coefficient (`int`, *optional*, defaults to 16):
Proportional gain written to the Feetech STS3215 motors at connect time.
position_i_coefficient (`int`, *optional*, defaults to 0):
Integral gain written to the motors at connect time.
position_d_coefficient (`int`, *optional*, defaults to 32):
Derivative gain written to the motors at connect time.
num_read_retries (`int`, *optional*, defaults to 2):
Extra attempts when a `sync_read` fails. Feetech buses occasionally return a corrupted status
packet, especially when several joints move at once, which would otherwise abort the control
loop. Retries are immediate and only happen on failure, so steady-state read cost is unchanged.
id (`str`, *optional*):
Identifier for this particular arm; also names its calibration file.
calibration_dir (`Path`, *optional*):
Where to read and write the calibration file. Defaults to the LeRobot calibration home.
Example:
```python
>>> from lerobot.robots.so_follower import SO101Follower, SO101FollowerConfig
>>> config = SO101FollowerConfig(port="/dev/ttyACM0", max_relative_target=5.0) # doctest: +SKIP
>>> robot = SO101Follower(config) # doctest: +SKIP
```
"""
pass
@@ -40,8 +40,7 @@ logger = logging.getLogger(__name__)
@ProcessorStepRegistry.register("ee_reference_and_delta")
@dataclass
class EEReferenceAndDelta(RobotActionProcessorStep):
"""
Computes a target end-effector pose from a relative delta command.
"""Computes a target end-effector pose from a relative delta command.
This step takes a desired change in position and orientation (`target_*`) and applies it to a
reference end-effector pose to calculate an absolute target pose. The reference pose is derived
@@ -53,15 +52,16 @@ class EEReferenceAndDelta(RobotActionProcessorStep):
2. `use_latched_reference=False`: The reference pose is updated to the robot's current pose at
every step.
Attributes:
kinematics: The robot's kinematic model for forward kinematics.
end_effector_step_sizes: A dictionary scaling the input delta commands.
motor_names: A list of motor names required for forward kinematics.
use_latched_reference: If True, latch the reference pose on enable; otherwise, always use the
current pose as the reference.
reference_ee_pose: Internal state storing the latched reference pose.
_prev_enabled: Internal state to detect the rising edge of the enable signal.
_command_when_disabled: Internal state to hold the last command while disabled.
**Attributes**:
- **kinematics** (`RobotKinematics`) -- The robot's kinematic model for forward kinematics.
- **end_effector_step_sizes** (`dict`) -- A dictionary scaling the input delta commands.
- **motor_names** (`list[str]`) -- A list of motor names required for forward kinematics.
- **use_latched_reference** (`bool`) -- If True, latch the reference pose on enable; otherwise, always
use the current pose as the reference.
- **reference_ee_pose** (`np.ndarray | None`) -- Internal state storing the latched reference pose.
- **_prev_enabled** (`bool`) -- Internal state to detect the rising edge of the enable signal.
- **_command_when_disabled** (`np.ndarray | None`) -- Internal state to hold the last command while
disabled.
"""
kinematics: RobotKinematics
@@ -77,6 +77,15 @@ class EEReferenceAndDelta(RobotActionProcessorStep):
_command_when_disabled: np.ndarray | None = field(default=None, init=False, repr=False)
def action(self, action: RobotAction) -> RobotAction:
"""Transform the action for this step.
Args:
action (`dict[str, Any]`):
The incoming robot action.
Returns:
`dict[str, Any]`: The transformed action.
"""
raw_observation = self.transition.get(TransitionKey.OBSERVATION)
if raw_observation is None:
@@ -167,6 +176,16 @@ class EEReferenceAndDelta(RobotActionProcessorStep):
def transform_features(
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
"""Update the feature contract to match what this step does to the data.
Args:
features (`dict[PipelineFeatureType, dict[str, PolicyFeature]]`):
The pipeline's feature contract so far.
Returns:
`dict[PipelineFeatureType, dict[str, PolicyFeature]]`: The contract with this step's key changes
applied.
"""
for feat in [
"enabled",
"target_x",
@@ -190,21 +209,19 @@ class EEReferenceAndDelta(RobotActionProcessorStep):
@ProcessorStepRegistry.register("ee_bounds_and_safety")
@dataclass
class EEBoundsAndSafety(RobotActionProcessorStep):
"""
Clips the end-effector pose to predefined bounds and checks for unsafe jumps.
"""Clips the end-effector pose to predefined bounds and checks for unsafe jumps.
This step ensures that the target end-effector pose remains within a safe operational workspace.
It also moderates the command to prevent large, sudden movements between consecutive steps.
Attributes:
end_effector_bounds: A dictionary with "min" and "max" keys for position clipping.
max_ee_step_m: The maximum allowed change in position (in meters) between steps.
raise_on_jump: When ``True`` (default) an over-limit per-frame step raises
``ValueError`` (aborting the control loop). When ``False`` the step is
rate-limited to ``max_ee_step_m`` and a warning is logged instead the
safer choice for live teleoperation, where a transient tracking glitch
should not crash the loop and leave the robot uncontrolled.
_last_pos: Internal state storing the last commanded position.
**Attributes**:
- **end_effector_bounds** (`dict`) -- A dictionary with "min" and "max" keys for position clipping.
- **max_ee_step_m** (`float`) -- The maximum allowed change in position (in meters) between steps.
- **raise_on_jump** (`bool`) -- When ``True`` (default) an over-limit per-frame step raises
``ValueError`` (aborting the control loop). When ``False`` the step is rate-limited to
``max_ee_step_m`` and a warning is logged instead the safer choice for live teleoperation, where a
transient tracking glitch should not crash the loop and leave the robot uncontrolled.
- **_last_pos** (`np.ndarray | None`) -- Internal state storing the last commanded position.
"""
end_effector_bounds: dict
@@ -213,6 +230,15 @@ class EEBoundsAndSafety(RobotActionProcessorStep):
_last_pos: np.ndarray | None = field(default=None, init=False, repr=False)
def action(self, action: RobotAction) -> RobotAction:
"""Transform the action for this step.
Args:
action (`dict[str, Any]`):
The incoming robot action.
Returns:
`dict[str, Any]`: The transformed action.
"""
x = action["ee.x"]
y = action["ee.y"]
z = action["ee.z"]
@@ -268,29 +294,39 @@ class EEBoundsAndSafety(RobotActionProcessorStep):
def transform_features(
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
"""Update the feature contract to match what this step does to the data.
Args:
features (`dict[PipelineFeatureType, dict[str, PolicyFeature]]`):
The pipeline's feature contract so far.
Returns:
`dict[PipelineFeatureType, dict[str, PolicyFeature]]`: The contract with this step's key changes
applied.
"""
return features
@ProcessorStepRegistry.register("inverse_kinematics_ee_to_joints")
@dataclass
class InverseKinematicsEEToJoints(RobotActionProcessorStep):
"""
Computes desired joint positions from a target end-effector pose using inverse kinematics (IK).
"""Computes desired joint positions from a target end-effector pose using inverse kinematics (IK).
This step translates a Cartesian command (position and orientation of the end-effector) into
the corresponding joint-space commands for each motor.
Attributes:
kinematics: The robot's kinematic model for inverse kinematics.
motor_names: A list of motor names for which to compute joint positions.
q_curr: Internal state storing the last joint positions, used as an initial guess for the IK solver.
initial_guess_current_joints: If True, use the robot's current joint state as the IK guess.
If False, use the solution from the previous step.
orientation_weight: Weight for the orientation constraint passed to
``RobotKinematics.inverse_kinematics``. Defaults to ``0.01`` (matching the solver
default, so existing callers are unchanged). Set to ``0.0`` for position-only IK on
under-actuated arms; a small nonzero weight gives soft-orientation IK on the 5-DOF
SO-101, where the wrist tracks orientation only partially (position dominates).
**Attributes**:
- **kinematics** (`RobotKinematics`) -- The robot's kinematic model for inverse kinematics.
- **motor_names** (`list[str]`) -- A list of motor names for which to compute joint positions.
- **q_curr** (`np.ndarray | None`) -- Internal state storing the last joint positions, used as an
initial guess for the IK solver.
- **initial_guess_current_joints** (`bool`) -- If True, use the robot's current joint state as the IK
guess. If False, use the solution from the previous step.
- **orientation_weight** (`float`) -- Weight for the orientation constraint passed to
``RobotKinematics.inverse_kinematics``. Defaults to ``0.01`` (matching the solver default, so
existing callers are unchanged). Set to ``0.0`` for position-only IK on under-actuated arms; a small
nonzero weight gives soft-orientation IK on the 5-DOF SO-101, where the wrist tracks orientation
only partially (position dominates).
"""
kinematics: RobotKinematics
@@ -300,6 +336,15 @@ class InverseKinematicsEEToJoints(RobotActionProcessorStep):
orientation_weight: float = 0.01
def action(self, action: RobotAction) -> RobotAction:
"""Transform the action for this step.
Args:
action (`dict[str, Any]`):
The incoming robot action.
Returns:
`dict[str, Any]`: The transformed action.
"""
x = action.pop("ee.x")
y = action.pop("ee.y")
z = action.pop("ee.z")
@@ -355,6 +400,16 @@ class InverseKinematicsEEToJoints(RobotActionProcessorStep):
def transform_features(
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
"""Update the feature contract to match what this step does to the data.
Args:
features (`dict[PipelineFeatureType, dict[str, PolicyFeature]]`):
The pipeline's feature contract so far.
Returns:
`dict[PipelineFeatureType, dict[str, PolicyFeature]]`: The contract with this step's key changes
applied.
"""
for feat in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]:
features[PipelineFeatureType.ACTION].pop(f"ee.{feat}", None)
@@ -373,20 +428,20 @@ class InverseKinematicsEEToJoints(RobotActionProcessorStep):
@ProcessorStepRegistry.register("gripper_velocity_to_joint")
@dataclass
class GripperVelocityToJoint(RobotActionProcessorStep):
"""
Converts a gripper velocity command into a target gripper joint position.
"""Converts a gripper velocity command into a target gripper joint position.
This step integrates a normalized velocity command over time to produce a position command,
taking the current gripper position as a starting point. It also supports a discrete mode
where integer actions map to open, close, or no-op.
Attributes:
motor_names: A list of motor names, which must include 'gripper'.
speed_factor: A scaling factor to convert the normalized velocity command to a position change.
clip_min: The minimum allowed gripper joint position.
clip_max: The maximum allowed gripper joint position.
discrete_gripper: If True, interpret the input as a discrete class index
{0 = close, 1 = stay, 2 = open}, matching `GamepadTeleop.GripperAction`.
**Attributes**:
- **motor_names** -- A list of motor names, which must include 'gripper'.
- **speed_factor** (`float`) -- A scaling factor to convert the normalized velocity command to a
position change.
- **clip_min** (`float`) -- The minimum allowed gripper joint position.
- **clip_max** (`float`) -- The maximum allowed gripper joint position.
- **discrete_gripper** (`bool`) -- If True, interpret the input as a discrete class index {0 = close,
1 = stay, 2 = open}, matching `GamepadTeleop.GripperAction`.
"""
speed_factor: float = 20.0
@@ -395,6 +450,15 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
discrete_gripper: bool = False
def action(self, action: RobotAction) -> RobotAction:
"""Transform the action for this step.
Args:
action (`dict[str, Any]`):
The incoming robot action.
Returns:
`dict[str, Any]`: The transformed action.
"""
raw_observation = self.transition.get(TransitionKey.OBSERVATION)
gripper_vel = action.pop("ee.gripper_vel")
@@ -428,6 +492,16 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
def transform_features(
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
"""Update the feature contract to match what this step does to the data.
Args:
features (`dict[PipelineFeatureType, dict[str, PolicyFeature]]`):
The pipeline's feature contract so far.
Returns:
`dict[PipelineFeatureType, dict[str, PolicyFeature]]`: The contract with this step's key changes
applied.
"""
features[PipelineFeatureType.ACTION].pop("ee.gripper_vel", None)
features[PipelineFeatureType.ACTION]["ee.gripper_pos"] = PolicyFeature(
type=FeatureType.ACTION, shape=(1,)
@@ -439,6 +513,21 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
def compute_forward_kinematics_joints_to_ee(
joints: dict[str, Any], kinematics: RobotKinematics, motor_names: list[str]
) -> dict[str, Any]:
"""Replace joint positions with the end-effector pose they produce.
Args:
joints (`dict[str, Any]`):
Joint values keyed `"<motor>.pos"`, including `"gripper.pos"`. Modified in place: the joint
keys named in `motor_names` are removed.
kinematics (`RobotKinematics`):
The arm's kinematic model.
motor_names (`list[str]`):
The motors, in the order the kinematic model expects them.
Returns:
`dict[str, Any]`: The same dict with `ee.x`, `ee.y`, `ee.z` for position, `ee.wx`, `ee.wy`,
`ee.wz` for orientation as a rotation vector, and `ee.gripper_pos` carried through unchanged.
"""
motor_joint_values = [joints[f"{n}.pos"] for n in motor_names]
q = np.array(motor_joint_values, dtype=float)
@@ -461,26 +550,44 @@ def compute_forward_kinematics_joints_to_ee(
@ProcessorStepRegistry.register("forward_kinematics_joints_to_ee_observation")
@dataclass
class ForwardKinematicsJointsToEEObservation(ObservationProcessorStep):
"""
Computes the end-effector pose from joint positions using forward kinematics (FK).
"""Computes the end-effector pose from joint positions using forward kinematics (FK).
This step is typically used to add the robot's Cartesian pose to the observation space,
which can be useful for visualization or as an input to a policy.
Attributes:
kinematics: The robot's kinematic model.
**Attributes**:
- **kinematics** (`RobotKinematics`) -- The robot's kinematic model.
"""
kinematics: RobotKinematics
motor_names: list[str]
def observation(self, observation: RobotObservation) -> RobotObservation:
"""Replace the observation's joint positions with the end-effector pose.
Args:
observation (`dict[str, Any]`):
The incoming observation, containing `"<motor>.pos"` keys.
Returns:
`dict[str, Any]`: The observation with `ee.*` keys in place of the joint positions.
"""
return compute_forward_kinematics_joints_to_ee(observation, self.kinematics, self.motor_names)
def transform_features(
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
# We only use the ee pose in the dataset, so we don't need the joint positions
"""Update the feature contract to match what this step does to the data.
Args:
features (`dict[PipelineFeatureType, dict[str, PolicyFeature]]`):
The pipeline's feature contract so far.
Returns:
`dict[PipelineFeatureType, dict[str, PolicyFeature]]`: The contract with this step's key changes
applied.
"""
for n in self.motor_names:
features[PipelineFeatureType.OBSERVATION].pop(f"{n}.pos", None)
# We specify the dataset features of this step that we want to be stored in the dataset
@@ -494,26 +601,44 @@ class ForwardKinematicsJointsToEEObservation(ObservationProcessorStep):
@ProcessorStepRegistry.register("forward_kinematics_joints_to_ee_action")
@dataclass
class ForwardKinematicsJointsToEEAction(RobotActionProcessorStep):
"""
Computes the end-effector pose from joint positions using forward kinematics (FK).
"""Computes the end-effector pose from joint positions using forward kinematics (FK).
This step is typically used to add the robot's Cartesian pose to the observation space,
which can be useful for visualization or as an input to a policy.
Attributes:
kinematics: The robot's kinematic model.
**Attributes**:
- **kinematics** (`RobotKinematics`) -- The robot's kinematic model.
"""
kinematics: RobotKinematics
motor_names: list[str]
def action(self, action: RobotAction) -> RobotAction:
"""Transform the action for this step.
Args:
action (`dict[str, Any]`):
The incoming robot action.
Returns:
`dict[str, Any]`: The transformed action.
"""
return compute_forward_kinematics_joints_to_ee(action, self.kinematics, self.motor_names)
def transform_features(
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
# We only use the ee pose in the dataset, so we don't need the joint positions
"""Update the feature contract to match what this step does to the data.
Args:
features (`dict[PipelineFeatureType, dict[str, PolicyFeature]]`):
The pipeline's feature contract so far.
Returns:
`dict[PipelineFeatureType, dict[str, PolicyFeature]]`: The contract with this step's key changes
applied.
"""
for n in self.motor_names:
features[PipelineFeatureType.ACTION].pop(f"{n}.pos", None)
# Store end-effector features as actions in the dataset schema
@@ -527,10 +652,21 @@ class ForwardKinematicsJointsToEEAction(RobotActionProcessorStep):
@ProcessorStepRegistry.register(name="forward_kinematics_joints_to_ee")
@dataclass
class ForwardKinematicsJointsToEE(ProcessorStep):
"""Applies forward kinematics to whichever of the action and observation are present.
A convenience wrapper over [`ForwardKinematicsJointsToEEAction`] and
[`ForwardKinematicsJointsToEEObservation`], so a pipeline needs one step instead of two.
**Attributes**:
- **kinematics** (`RobotKinematics`) -- The arm's kinematic model.
- **motor_names** (`list[str]`) -- The motors, in the order the kinematic model expects them.
"""
kinematics: RobotKinematics
motor_names: list[str]
def __post_init__(self):
"""Build the action and observation sub-steps this step delegates to."""
self.joints_to_ee_action_processor = ForwardKinematicsJointsToEEAction(
kinematics=self.kinematics, motor_names=self.motor_names
)
@@ -539,6 +675,15 @@ class ForwardKinematicsJointsToEE(ProcessorStep):
)
def __call__(self, transition: EnvTransition) -> EnvTransition:
"""Apply forward kinematics to whichever of the action and observation are present.
Args:
transition (`EnvTransition`):
The transition to transform.
Returns:
`EnvTransition`: The transition with `ee.*` keys in place of joint positions.
"""
if transition.get(TransitionKey.ACTION) is not None:
transition = self.joints_to_ee_action_processor(transition)
if transition.get(TransitionKey.OBSERVATION) is not None:
@@ -548,6 +693,16 @@ class ForwardKinematicsJointsToEE(ProcessorStep):
def transform_features(
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
"""Update the feature contract to match what this step does to the data.
Args:
features (`dict[PipelineFeatureType, dict[str, PolicyFeature]]`):
The pipeline's feature contract so far.
Returns:
`dict[PipelineFeatureType, dict[str, PolicyFeature]]`: The contract with this step's key changes
applied.
"""
if features[PipelineFeatureType.ACTION] is not None:
features = self.joints_to_ee_action_processor.transform_features(features)
if features[PipelineFeatureType.OBSERVATION] is not None:
@@ -558,8 +713,7 @@ class ForwardKinematicsJointsToEE(ProcessorStep):
@ProcessorStepRegistry.register("inverse_kinematics_rl_step")
@dataclass
class InverseKinematicsRLStep(ProcessorStep):
"""
Computes desired joint positions from a target end-effector pose using inverse kinematics (IK).
"""Computes desired joint positions from a target end-effector pose using inverse kinematics (IK).
This is modified from the InverseKinematicsEEToJoints step to be used in the RL pipeline.
"""
@@ -570,6 +724,15 @@ class InverseKinematicsRLStep(ProcessorStep):
initial_guess_current_joints: bool = True
def __call__(self, transition: EnvTransition) -> EnvTransition:
"""Solve inverse kinematics for the transition's end-effector action.
Args:
transition (`EnvTransition`):
The transition to transform.
Returns:
`EnvTransition`: The transition with joint targets in place of the `ee.*` action.
"""
new_transition = dict(transition)
action = new_transition.get(TransitionKey.ACTION)
if action is None:
@@ -633,6 +796,16 @@ class InverseKinematicsRLStep(ProcessorStep):
def transform_features(
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
"""Update the feature contract to match what this step does to the data.
Args:
features (`dict[PipelineFeatureType, dict[str, PolicyFeature]]`):
The pipeline's feature contract so far.
Returns:
`dict[PipelineFeatureType, dict[str, PolicyFeature]]`: The contract with this step's key changes
applied.
"""
for feat in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]:
features[PipelineFeatureType.ACTION].pop(f"ee.{feat}", None)
+87 -8
View File
@@ -35,15 +35,35 @@ logger = logging.getLogger(__name__)
class SOFollower(Robot):
"""
Generic SO follower base implementing common functionality for SO-100/101/10X.
Designed to be subclassed with a per-hardware-model `config_class` and `name`.
"""The SO-family follower arm: a 5-DOF arm plus gripper on a Feetech bus.
`SO100Follower` and `SO101Follower` are aliases of this class. The two arms differ in calibration and
gearing, not control code, so both are driven through the same implementation with a different
`config_class` and `name`.
Actions and observations are keyed `"<motor>.pos"`; cameras named in the config appear in observations
under their own keys. See [`~robots.Robot`] for the contract every method here implements.
Example:
```python
>>> from lerobot.robots.so_follower import SO101Follower, SO101FollowerConfig
>>> robot = SO101Follower(SO101FollowerConfig(port="/dev/ttyACM0")) # doctest: +SKIP
>>> with robot: # doctest: +SKIP
... observation = robot.get_observation()
... robot.send_action({"shoulder_pan.pos": 0.0})
```
"""
config_class = SOFollowerRobotConfig
name = "so_follower"
def __init__(self, config: SOFollowerRobotConfig):
"""Build the robot from its configuration.
Args:
config (`SOFollowerRobotConfig`):
The robot's configuration. Its `port` and `cameras` determine what is connected.
"""
super().__init__(config)
self.config = config
# choose normalization mode depending on config if available
@@ -78,23 +98,48 @@ class SOFollower(Robot):
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
"""The arm's joint positions plus one entry per configured camera.
Returns:
`dict[str, type | tuple]`: `"<motor>.pos"` keys mapped to `float`, and one key per camera
mapped to its `(height, width, channels)` shape.
"""
return {**self._motors_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
"""The arm's goal joint positions.
Returns:
`dict[str, type]`: `"<motor>.pos"` keys mapped to `float`.
"""
return self._motors_ft
@property
def is_connected(self) -> bool:
"""Whether the motor bus and every configured camera are connected.
Returns:
`bool`: `True` only when all of them are.
"""
return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
"""
We assume that at connection time, arm is in a rest position,
and torque can be safely disabled to run calibration.
"""
"""Connect the motor bus and cameras, calibrating and configuring the arm.
> [!WARNING]
> The arm is assumed to be at rest when this is called, because torque is disabled to run
> calibration. Do not call it with the arm holding a load.
Args:
calibrate (`bool`, *optional*, defaults to `True`):
Whether to run calibration when the motors disagree with the calibration file, or no file
exists yet. Calibration is interactive and prompts on stdin.
Raises:
DeviceAlreadyConnectedError: If the robot is already connected.
"""
self.bus.connect()
if not self.is_calibrated and calibrate:
logger.info(
@@ -110,9 +155,19 @@ class SOFollower(Robot):
@property
def is_calibrated(self) -> bool:
"""Whether the motors' stored calibration matches the calibration file.
Returns:
`bool`: `True` when the arm needs no recalibration.
"""
return self.bus.is_calibrated
def calibrate(self) -> None:
"""Calibrate the arm, writing the result to the motors and the calibration file.
This is interactive: it prompts on stdin to reuse an existing calibration file, and otherwise asks
you to move the arm to its middle position and then through each joint's full range.
"""
if self.calibration:
# Calibration file exists, ask user whether to use it or run new calibration
user_input = input(
@@ -157,6 +212,11 @@ class SOFollower(Robot):
print("Calibration saved to", self.calibration_fpath)
def configure(self) -> None:
"""Write the position-mode operating mode and the configured PID gains to every motor.
The gripper additionally gets reduced torque, current and overload limits so that gripping a rigid
object does not burn out its motor.
"""
with self.bus.torque_disabled():
self.bus.configure_motors()
for motor in self.bus.motors:
@@ -171,6 +231,11 @@ class SOFollower(Robot):
self.bus.write("Overload_Torque", motor, 25) # 25% torque when overloaded
def setup_motors(self) -> None:
"""Assign each motor its bus ID, one at a time.
Run this once when building an arm. It is interactive: it prompts you to connect the controller
board to a single motor at a time, working from the gripper back to the base.
"""
for motor in reversed(self.bus.motors):
input(f"Connect the controller board to the '{motor}' motor only and press enter.")
self.bus.setup_motor(motor)
@@ -178,6 +243,14 @@ class SOFollower(Robot):
@check_if_not_connected
def get_observation(self) -> RobotObservation:
"""Read the arm's joint positions and one frame from each camera.
Returns:
`dict[str, Any]`: Keys matching [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
# Read arm position
start = time.perf_counter()
obs_dict = self.bus.sync_read("Present_Position", num_retry=self.config.num_read_retries)
@@ -215,7 +288,6 @@ class SOFollower(Robot):
Returns:
RobotAction: the action sent to the motors, potentially clipped.
"""
goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
# Cap goal position when too far away from present position.
@@ -231,6 +303,13 @@ class SOFollower(Robot):
@check_if_not_connected
def disconnect(self):
"""Disconnect the motor bus and every camera.
Torque is released first unless `disable_torque_on_disconnect` is `False`.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
self.bus.disconnect(self.config.disable_torque_on_disconnect)
for cam in self.cameras.values():
cam.disconnect()
@@ -47,6 +47,42 @@ _DEFAULT_KP, _DEFAULT_KD = _build_gains()
@RobotConfig.register_subclass("unitree_g1")
@dataclass
class UnitreeG1Config(RobotConfig):
"""Configuration for the Unitree G1 humanoid.
The G1 is reached over a ZMQ bridge rather than a serial bus, so there is no `port` field and
calibration is handled by the robot's own firmware.
All 29 joints are addressed by index, so `kp`, `kd` and `default_positions` are lists in the G1's joint
order: left leg, right leg, waist, left arm, left wrist, right arm, right wrist.
Args:
kp (`list[float]`, *optional*):
Per-joint proportional gains, 29 values. Defaults to the per-body-part gains recommended by
Unitree.
kd (`list[float]`, *optional*):
Per-joint derivative gains, 29 values.
default_positions (`list[float]`, *optional*):
Per-joint home positions, 29 values. Defaults to all zeros.
control_dt (`float`, *optional*, defaults to 0.004):
Control loop timestep in seconds, i.e. 250 Hz.
is_simulation (`bool`, *optional*, defaults to `True`):
Whether to drive a MuJoCo simulation instead of the physical robot. Keep `True` until the
behaviour is validated in sim.
robot_ip (`str`, *optional*, defaults to `"192.168.123.164"`):
Address of the robot's ZMQ bridge. The default is the G1's factory address.
cameras (`dict[str, CameraConfig]`, *optional*):
ZMQ-based remote cameras to read alongside the joint states.
gravity_compensation (`bool`, *optional*, defaults to `False`):
Whether to compensate for gravity on the arms using the arm IK solver.
controller (`str`, *optional*):
Class name of the lower-body locomotion controller, e.g. `"GrootLocomotionController"` or
`"HolosomaLocomotionController"`. `None` leaves the legs uncontrolled.
id (`str`, *optional*):
Identifier for this particular robot.
calibration_dir (`Path`, *optional*):
Unused: the G1 manages its own calibration.
"""
kp: list[float] = field(default_factory=lambda: _DEFAULT_KP.copy())
kd: list[float] = field(default_factory=lambda: _DEFAULT_KD.copy())
@@ -24,7 +24,17 @@ logger = logging.getLogger(__name__)
class WeightedMovingFilter:
"""A fixed-length weighted moving average over recent samples, used to smooth IK solutions."""
def __init__(self, weights, data_size=14):
"""Set up the filter.
Args:
weights:
Per-sample weights, newest first. Their length sets the window size.
data_size (`int`, *optional*, defaults to 14):
Number of values in each sample.
"""
self._window_size = len(weights)
self._weights = np.array(weights)
self._data_size = data_size
@@ -39,6 +49,12 @@ class WeightedMovingFilter:
return data_array.T @ self._weights
def add_data(self, new_data):
"""Push a sample into the window and recompute the filtered value.
Args:
new_data:
A sample of length `data_size`. Ignored if identical to the newest one already held.
"""
assert len(new_data) == self._data_size
if len(self._data_queue) > 0 and np.array_equal(
@@ -51,11 +67,24 @@ class WeightedMovingFilter:
@property
def filtered_data(self):
"""The current weighted average.
Returns:
`np.ndarray`: The filtered sample.
"""
return self._filtered_data
class G1_29_ArmIK: # noqa: N801
"""Inverse kinematics for the G1's two arms, solved together as one optimisation problem."""
def __init__(self, unit_test=False):
"""Build the arm model and the IK solver.
Args:
unit_test (`bool`, *optional*, defaults to `False`):
Whether to run in test mode, which visualises the solution instead of driving a robot.
"""
import casadi
import pinocchio as pin
from huggingface_hub import snapshot_download
@@ -230,6 +259,21 @@ class G1_29_ArmIK: # noqa: N801
self.smooth_filter = WeightedMovingFilter(np.array([0.4, 0.3, 0.2, 0.1]), 14)
def solve_ik(self, left_wrist, right_wrist, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None):
"""Solve for the arm joint angles that place both wrists at the requested poses.
Args:
left_wrist:
Target pose of the left wrist as a 4x4 homogeneous transform.
right_wrist:
Target pose of the right wrist as a 4x4 homogeneous transform.
current_lr_arm_motor_q (*optional*):
Present arm joint positions, used as the solver's initial guess.
current_lr_arm_motor_dq (*optional*):
Present arm joint velocities, used to compute feed-forward torques.
Returns:
`tuple`: The solved joint positions and the corresponding torques.
"""
if current_lr_arm_motor_q is not None:
self.init_data = current_lr_arm_motor_q
self.opti.set_initial(self.var_q, self.init_data)
@@ -268,6 +312,17 @@ class G1_29_ArmIK: # noqa: N801
return sol_q, sol_tauff
def solve_tau(self, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None):
"""Compute the gravity-compensating torques for the arms at a given state.
Args:
current_lr_arm_motor_q (*optional*):
Present arm joint positions.
current_lr_arm_motor_dq (*optional*):
Present arm joint velocities.
Returns:
`np.ndarray`: Per-joint torques.
"""
try:
q_g1 = np.array(current_lr_arm_motor_q, dtype=float)
if q_g1.shape[0] != len(self._arm_joint_names_g1):
@@ -44,6 +44,8 @@ def get_gravity_orientation(quaternion: list[float] | np.ndarray) -> np.ndarray:
class G1_29_JointArmIndex(IntEnum):
"""Indices of the G1's arm and wrist joints within its 29-joint state vector."""
# Left arm
kLeftShoulderPitch = 15
kLeftShoulderRoll = 16
@@ -79,6 +81,8 @@ def make_locomotion_controller(name: str | None):
class G1_29_JointIndex(IntEnum):
"""Indices of all 29 G1 joints, in the order the robot reports and accepts them."""
# Left leg
kLeftHipPitch = 0
kLeftHipRoll = 1
@@ -83,6 +83,7 @@ class GrootLocomotionController:
control_dt = CONTROL_DT # Expose for unitree_g1.py
def __init__(self):
"""Load the GR00T locomotion policy and set up its observation history."""
# Load policies
self.policy_balance, self.policy_walk = load_groot_policies()
@@ -101,6 +101,7 @@ class HolosomaLocomotionController:
control_dt = CONTROL_DT # Expose for unitree_g1.py
def __init__(self):
"""Load the HoloSoma locomotion policy and set up its observation history."""
# Load policy and gains
self.policy, self.kp, self.kd = load_policy()
@@ -14,8 +14,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
DDS-to-ZMQ bridge server for Unitree G1 robot.
"""DDS-to-ZMQ bridge server for Unitree G1 robot.
This server runs on the robot and forwards:
- Robot state (LowState) from DDS to ZMQ (for remote clients)
+132 -2
View File
@@ -67,11 +67,27 @@ logger = logging.getLogger(__name__)
@runtime_checkable
class LocomotionController(Protocol):
"""The interface a lower-body locomotion controller must provide to drive the G1's legs."""
control_dt: float
def run_step(self, action: dict, lowstate) -> dict: ...
def run_step(self, action: dict, lowstate) -> dict:
"""Compute one step of leg commands.
def reset(self) -> None: ...
Args:
action (`dict`):
The upper-body action and locomotion command for this step.
lowstate:
The robot's most recent low-level state.
Returns:
`dict`: Leg joint targets for this step.
"""
...
def reset(self) -> None:
"""Clear any internal state, e.g. an observation history, before a new episode."""
...
# DDS topic names follow Unitree SDK naming conventions
@@ -82,6 +98,8 @@ kTopicLowState = "rt/lowstate"
@dataclass
class MotorState:
"""One motor's reported position, velocity and torque."""
q: float | None = None # position
dq: float | None = None # velocity
tau_est: float | None = None # estimated torque
@@ -90,6 +108,8 @@ class MotorState:
@dataclass
class IMUState:
"""The G1's inertial measurements: orientation, angular velocity and acceleration."""
quaternion: np.ndarray | None = None # [w, x, y, z]
gyroscope: np.ndarray | None = None # [x, y, z] angular velocity (rad/s)
accelerometer: np.ndarray | None = None # [x, y, z] linear acceleration (m/s²)
@@ -100,6 +120,8 @@ class IMUState:
# g1 observation class
@dataclass
class G1_29_LowState: # noqa: N801
"""A full low-level state frame: every motor's state plus the IMU."""
motor_state: list[MotorState] = field(default_factory=lambda: [MotorState() for _ in G1_29_JointIndex])
imu_state: IMUState = field(default_factory=IMUState)
wireless_remote: bytes | None = None # Raw wireless remote data
@@ -107,10 +129,29 @@ class G1_29_LowState: # noqa: N801
class UnitreeG1(Robot):
"""The Unitree G1 humanoid, driven over a ZMQ bridge.
Upper-body joints are commanded directly. The legs are handled by an optional locomotion controller
named in the config, which runs its own loop against the robot's low-level state. Set
`is_simulation=True` to drive a MuJoCo model instead of the physical robot.
See [`~robots.Robot`] for the contract every method here implements.
"""
config_class = UnitreeG1Config
name = "unitree_g1"
def __init__(self, config: UnitreeG1Config):
"""Build the robot and, if one is configured, its locomotion controller.
Args:
config (`UnitreeG1Config`):
The robot's configuration, including gains, the ZMQ bridge address and whether to run
against MuJoCo instead of hardware.
Raises:
ImportError: If the `unitree_g1` extra is not installed.
"""
require_package("unitree-sdk2py", extra="unitree_g1", import_name="unitree_sdk2py")
super().__init__(config)
@@ -204,6 +245,18 @@ class UnitreeG1(Robot):
kd: np.ndarray | list[float] | None = None,
tau: np.ndarray | list[float] | None = None,
) -> None: # writes robot command whenever requested
"""Write a low-level command frame to the robot.
Args:
action (`dict[str, Any]`):
Target joint positions for this step.
kp (`np.ndarray | list[float]`, *optional*):
Per-joint proportional gains. Defaults to the config's `kp`.
kd (`np.ndarray | list[float]`, *optional*):
Per-joint derivative gains. Defaults to the config's `kd`.
tau (`np.ndarray | list[float]`, *optional*):
Per-joint feed-forward torques. Defaults to zero.
"""
for motor in G1_29_JointIndex:
key = f"{motor.name}.q"
if key in action:
@@ -233,10 +286,21 @@ class UnitreeG1(Robot):
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
"""The values this robot reports, and their types or shapes.
Returns:
`dict`: Keys as returned by [`~robots.Robot.get_observation`], mapped to a scalar type for
proprioceptive values or to a `(height, width, channels)` shape for images.
"""
return {**self._motors_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
"""The values this robot accepts, and their types.
Returns:
`dict`: Keys accepted by [`~robots.Robot.send_action`], mapped to their type.
"""
if self.controller is None:
return {f"{G1_29_JointIndex(motor).name}.q": float for motor in G1_29_JointIndex}
@@ -288,13 +352,27 @@ class UnitreeG1(Robot):
def calibrate(self) -> None:
# TODO: implement g1_29 calibration
"""Calibrate the robot and store the result.
Interactive: prompts on stdin and asks you to move the robot through the required positions.
"""
pass
def configure(self) -> None:
"""Apply the operating mode, gains and limits from the configuration to the robot."""
pass
def connect(self, calibrate: bool = True) -> None: # connect to DDS
# Initialize DDS channel and simulation environment
"""Connect to the robot and its cameras, then apply the configured settings.
Args:
calibrate (`bool`, *optional*, defaults to `True`):
Whether to run calibration if the robot is not already calibrated.
Raises:
DeviceAlreadyConnectedError: If the robot is already connected.
"""
if self.config.is_simulation:
from lerobot.envs import make_env
@@ -373,6 +451,11 @@ class UnitreeG1(Robot):
def disconnect(self):
# Put robot in passive mode before stopping threads
"""Disconnect from the robot and its cameras.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
if not self.config.is_simulation:
self._send_zero_torque()
@@ -417,6 +500,14 @@ class UnitreeG1(Robot):
cam.disconnect()
def get_observation(self) -> RobotObservation:
"""Read the robot's current state and a frame from each camera.
Returns:
`dict[str, Any]`: Keys matching [`~robots.Robot.observation_features`].
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
with self._lowstate_lock:
lowstate = self._lowstate
if lowstate is None:
@@ -471,6 +562,18 @@ class UnitreeG1(Robot):
return obs
def send_action(self, action: RobotAction) -> RobotAction:
"""Command the robot to move towards a target configuration.
Args:
action (`dict[str, Any]`):
Target values, keyed as in [`~robots.Robot.action_features`].
Returns:
`dict[str, Any]`: The action actually sent, which may be clipped by `max_relative_target`.
Raises:
DeviceNotConnectedError: If the robot is not connected.
"""
action_to_publish = action
if self.controller is not None:
# Controller thread owns legs/waist. Here we only update joystick inputs
@@ -511,10 +614,20 @@ class UnitreeG1(Robot):
@property
def is_calibrated(self) -> bool:
"""Whether the robot is calibrated.
Returns:
`bool`: `True` when no calibration is needed before use.
"""
return True
@property
def is_connected(self) -> bool:
"""Whether every device this robot uses is connected.
Returns:
`bool`: `True` only when the robot and all its cameras are connected.
"""
with self._lowstate_lock:
return self._lowstate is not None
@@ -525,6 +638,11 @@ class UnitreeG1(Robot):
@property
def cameras(self) -> dict:
"""The robot's configured cameras.
Returns:
`dict`: Camera name mapped to its instance.
"""
return self._cameras
def reset(
@@ -532,6 +650,18 @@ class UnitreeG1(Robot):
control_dt: float | None = None,
default_positions: list[float] | None = None,
) -> None: # move robot to default position
"""Move the robot smoothly to its default joint positions.
> [!WARNING]
> This drives every joint, legs included. Make sure the robot is supported or in a safe posture
> before calling it.
Args:
control_dt (`float`, *optional*):
Control loop timestep for the move. Defaults to the config's `control_dt`.
default_positions (`list[float]`, *optional*):
Target positions, 29 values in joint order. Defaults to the config's `default_positions`.
"""
if control_dt is None:
control_dt = self.config.control_dt
if default_positions is None:
@@ -37,8 +37,7 @@ kTopicLowCommand_Debug = "rt/lowcmd"
class LowStateMsg:
"""
Wrapper class that mimics the Unitree SDK LowState_ message structure.
"""Wrapper class that mimics the Unitree SDK LowState_ message structure.
Reconstructs the message from deserialized JSON data to maintain
compatibility with existing code that expects SDK message objects.
@@ -48,6 +47,12 @@ class LowStateMsg:
"""Motor state data for a single joint."""
def __init__(self, data: dict[str, Any]) -> None:
"""Build one motor's state from a deserialized JSON frame.
Args:
data (`dict[str, Any]`):
The motor's entry from the robot's state message.
"""
self.q: float = data.get("q", 0.0)
self.dq: float = data.get("dq", 0.0)
self.tau_est: float = data.get("tau_est", 0.0)
@@ -57,6 +62,12 @@ class LowStateMsg:
"""IMU sensor data."""
def __init__(self, data: dict[str, Any]) -> None:
"""Build the IMU state from a deserialized JSON frame.
Args:
data (`dict[str, Any]`):
The IMU's entry from the robot's state message.
"""
self.quaternion: list[float] = data.get("quaternion", [1.0, 0.0, 0.0, 0.0])
self.gyroscope: list[float] = data.get("gyroscope", [0.0, 0.0, 0.0])
self.accelerometer: list[float] = data.get("accelerometer", [0.0, 0.0, 0.0])
@@ -100,15 +111,16 @@ def lowcmd_to_dict(topic: str, msg: Any) -> dict[str, Any]:
def ChannelFactoryInitialize(domain_id: int = 0, config: Any = None) -> None: # noqa: N802
"""
Initialize ZMQ sockets for robot communication.
"""Initialize ZMQ sockets for robot communication.
This function mimics the Unitree SDK's ChannelFactoryInitialize but uses
ZMQ sockets to connect to the robot server bridge instead of DDS.
Args:
domain_id: Ignored (for API compatibility with Unitree SDK)
config: UnitreeG1Config instance with robot_ip
domain_id (`int`, *optional*, defaults to 0):
Ignored. Accepted only for API compatibility with the Unitree SDK.
config (`Any`, *optional*):
A `UnitreeG1Config` supplying `robot_ip`. Defaults to a fresh `UnitreeG1Config` when `None`.
"""
global _ctx, _lowcmd_sock, _lowstate_sock
@@ -138,6 +150,14 @@ class ChannelPublisher:
"""ZMQ-based publisher that sends commands to the robot server."""
def __init__(self, topic: str, msg_type: type) -> None:
"""Bind the publisher to a topic.
Args:
topic (`str`):
The topic name to publish under.
msg_type (`type`):
The message class this topic carries.
"""
self.topic = topic
self.msg_type = msg_type
@@ -158,6 +178,14 @@ class ChannelSubscriber:
"""ZMQ-based subscriber that receives state from the robot server."""
def __init__(self, topic: str, msg_type: type) -> None:
"""Bind the subscriber to a topic.
Args:
topic (`str`):
The topic name to receive from.
msg_type (`type`):
The message class this topic carries.
"""
self.topic = topic
self.msg_type = msg_type

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