Eagerly resolving every DEFAULT_BINDINGS entry made rendering fail on
frames whose events a default binding cannot disambiguate, e.g. the
camera-less vqa default on multi-camera frames, even when the recipe
never references that binding. Add TrainingRecipe.referenced_binding_names()
and skip bindings the recipe does not consume.
Split out of #4183 so the data-layer fix lands independently of the
language runtime.
Refs #4183
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* 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.
* ci: build the docs on Python 3.12
The shared doc-builder workflows create their virtualenv with the runner's
system Python, which is 3.10.12 on ubuntu-22.04. lerobot requires >=3.12, so
the build died during "Setup environment":
× No solution found when resolving dependencies:
╰─▶ Because the current Python version (3.10.12) does not satisfy
Python>=3.12 and lerobot==0.6.2 depends on Python>=3.12 ...
That step runs before `pre_command`, so the real install this workflow already
performs never got the chance to run. There was no fix available on the caller
side either: `env:` does not propagate into a reusable workflow, so `UV_PYTHON`
is unavailable, and `uv venv` runs in the runner workspace root rather than the
checkout, so a `.python-version` file cannot reach it. The non-light fallback
(`uv pip install "./pkg[dev]"`) fails identically, so this is not specific to
the mock-deps path — it blocks any package requiring 3.12+.
huggingface/doc-builder#808 adds a `python_version` input to both build
workflows, which this passes. Pins move to that merge commit, picking up three
unrelated fixes in the same range (#810, #811, #812); the upload workflow is
unchanged there and is bumped only to keep all three pins on one SHA.
* chore: sort imports in vla_jepa tests
Enabling pydocstyle in the previous commit changes how ruff determines where a
module's import block ends, which makes I001 fire on three vla_jepa tests that
were clean before. The blank line between the `conftest` and `lerobot` imports
is the trigger: both are first-party, so isort wants them in one contiguous
block, and the docstring-aware analysis is what makes it notice.
These files are unrelated to the API reference, so the fix is only to satisfy
the new gate.
The FSDP multi-GPU test still generated an `accelerate launch --config_file`
FSDP1 YAML, which exports ACCELERATE_USE_FSDP into the workers. Since the
FSDP2/parallelism rewrite, `guard_against_env_interference()` hard-errors on
exactly that variable, so the test failed on every rank. Its assertions were
stale too: sharded runs now write DCP optimizer shards, not a gathered
`optimizer_state.safetensors`.
Drop the YAML generation entirely and use `accelerate launch` as the plain
launcher the docs describe, with the topology coming from `--parallelism.*`.
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
TruffleHog's Lob API-key detector matches `\b((live|test)_[a-zA-Z0-9_]{35})\b`.
One EMA config test was named `test_` plus exactly 35 word characters, so it
matched, and Lob's verifier treats the 403/422 that api.lob.com returns for junk
keys as proof of a live key. That failed the Security workflow on main after
huggingface/lerobot#4323 with "Found verified Lob result" and exit code 183.
Add one word to the name so the suffix is 39 characters rather than 35. No
behavior change. Note that the offending string is deliberately not spelled out
here: TruffleHog scans commit messages as well as added lines, so quoting it
would retrigger the very detector this commit is working around.
Refs #4323
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
* feat(train): add opt-in EMA of the policy weights (--ema.enable=true)
Maintain an EMA shadow via diffusers' EMAModel (lazy import, no new
dependency) with the reference Diffusion Policy schedule. Saves the
shadow for exact resume plus a loadable pretrained_model_ema/ per
checkpoint, evaluates the EMA weights during env eval, and pushes them
to a sibling <repo_id>-ema repo. Fixeshuggingface/lerobot#4259.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* docs(diffusion): document the --ema.enable training flag
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(tests): skip EMA training tests when accelerate/diffusers are missing
* feat(train): support constant EMA decay (--ema.decay) for openpi-style policies
* fix(train): gate EMA step on sync_gradients; use parallel_dims.is_sharded guard
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Two post-merge CI failures on main, both from #4010.
Benchmark Integration Tests (Libero) — `accelerate launch` exports whole groups
of variables unconditionally (the five ACCELERATE_DYNAMO_* it writes default the
backend to "no"), so matching on prefixes refused launches that configure
nothing, contradicting the documented flow where accelerate is supported as a
plain launcher. The guard now watches only the three switches that hand a
subsystem to the environment.
GPU Tests — `test_metrics_tracker_reduce_across_ranks_invokes_all_reduce`
compared the captured reduction buffer against a CPU tensor, so the assert
raised "Expected all tensors to be on the same device" wherever CUDA is
available. The expected tensor is built on the buffer's device instead.
* feat(train): parallel training engine with FSDP2, HSDP, and DCP checkpoints
Replace the FSDP1 training path with a config-owned parallel-training
engine:
- Topology and runtime configs (--parallelism.*, --accelerator.*):
dp_replicate x dp_shard degrees select single-process, DDP (unchanged
default), FSDP2, or HSDP; mixed precision, first-class gradient
accumulation, and FSDP/DDP tuning knobs are mirrored as plain
dataclasses that build the accelerate objects at runtime, so every
run is reproducible from its train_config.json alone. Accelerate env
vars are guarded against configuring the engine behind the config
system's back.
- Declarative policy surface: policies declare FSDP2 wrap units
(_fsdp_wrap_modules) and non-forward entry points
(_fsdp_forward_methods); a shared engine resolves them around
accelerator.prepare(). Context-parallel fields are reserved and
validated to 1.
- Checkpoints: selectable --checkpoint_format (safetensors | dcp |
safetensors_dcp); the sharded optimizer channel is always DCP;
two-phase resume (step+RNG before prepare, DCP model/optimizer after)
reshards across GPU-topology changes; lerobot-convert-dcp merges DCP
shards into a distributable model.safetensors offline.
- Publishing: PreTrainedPolicy.push_model_to_hub is replaced by the
free publish_trained_model (model + processors + card + train config,
all-ranks gather with main-rank writes);
PreTrainedPolicy._save_pretrained gathers state dicts internally,
removing the state_dict= threading from save_pretrained.
- lerobot_train is restructured around the engine: optimizer built
before the single prepare() call, deferred weight load on DCP
resumes, collective save_checkpoint with no call-site rank branches,
dp-world-size-based sample accounting.
Breaking changes: FSDP checkpoints from lerobot <= 0.6.x are not
resumable (weights stay loadable via from_pretrained; pin
lerobot==0.6.x to finish old runs); the `accelerate launch
--config_file` yaml flow is superseded by the config flags; training
autocast is owned exclusively by --accelerator.mixed_precision
(policy.dtype only casts parameters).
Also fixes: reward-model hub publishing crash (TypeError on extra
kwargs).
Verified by ~200 new CPU tests (config round-trips, checkpoint
round-trips per format, two-phase resume, publisher contracts,
converter equivalence, accelerate canaries), a 5-test 4-GPU suite
(FSDP2 save/resume bit-exactness, HSDP/DDP loss parity,
changed-topology resume, all-ranks save_pretrained, grad-accum
equivalence), and end-to-end ACT (1/4/8 GPUs) + FastWAM 6B
(FSDP2 + HSDP) training runs.
* fix(sarm): warn when dense/sparse targets silently collapse to all-zero
In dense_only/dual modes, if meta/episodes/*.parquet has no usable
subtask columns (column absent or NaN), _load_episode_annotations
returns None and find_stage_and_tau yields stage 0 / tau 0 for every
frame. Training "succeeds" but the head silently learns to predict 0
everywhere, with no warning. This complements #2880 (which restored
loading of episodes_df): there the DataFrame is loaded but the
*_subtask_names column is missing/NaN.
Add a one-time validation at processor construction that logs a clear
warning (all episodes missing -> predict-all-zero; some missing ->
partial). Purely additive logging, no change to training math.
Closes#3842
* fix(sarm): fail fast on unusable episode annotations
---------
Co-authored-by: 1thanShih <Smartshithan1620.en12@nycu.edu.tw>
* fix(cameras): add color_format config and auto-recovery for RealSense D405
The D405 delivers color from its stereo depth module, not a dedicated
RGB sensor. The driver previously hardcoded rs.format.rgb8, causing
silent frame capture failure on D405.
Changes:
- Add color_format field to RealSenseCameraConfig (default rgb8, D405
users set bgr8), validated against whitelist
- Use configured format in _configure_rs_pipeline_config
- Fix _postprocess_image to handle both rgb8 and bgr8 source formats
- Add _hardware_reset auto-recovery: if warmup times out, perform USB
hardware reset and retry once (common D405 recovery path)
- Fix thread race in _read_loop (local ref to stop_event)
Continuation of #3164 (closed due to deleted fork).
Tested on Intel RealSense D405 at 1280x720@30fps with color_format=bgr8.
* style: fix ruff lint B904 and format
* refactor(cameras): clarify RealSense connection retry
* refactor(cameras): address review, retry RealSense connect before hardware reset
Drop color_format (device-side streaming state was the actual cause), keep _open_pipeline attempt-agnostic, catch only retry-worthy errors, reset only as last resort, guard read loop against late frame publication after stop.
* refactor(cameras): restore BaseException teardown, shorten stop-check comment
* test(cameras): expect ConnectionError after retries are exhausted
rollout() runs `while not np.all(done)` with `done` latched, so a sub-env that
terminates early keeps being driven -- physics and offscreen rendering included
-- until the slowest sub-env in the batch finishes. A batch of N runs for
max(episode_lengths) iterations to complete work that only needs
mean(episode_lengths), and all of the surplus is discarded.
Adds FreezeAfterEpisodeEnd, applied to each sub-env of the eval vector env. It
caches the terminal transition and replays it for any further step(), and also
absorbs Gymnasium's autoreset -- under AutoresetMode.NEXT_STEP the vector env
otherwise rebuilds a finished sub-env and runs it through an entire extra episode
the rollout throws away. Reward is zeroed on replay so a frozen sub-env cannot
inflate a return if a caller sums over the padded tail.
Thawing is signalled explicitly: rollout() passes NEW_ROLLOUT_OPTION in
reset(options=...). Gymnasium's autoreset calls reset() with no arguments, but so
would a caller passing seeds=None, and inferring from that would strand an env
frozen for a whole rollout.
AutoresetMode.DISABLED is not an alternative -- Gymnasium asserts that no
terminated env is ever stepped in that mode, so the wrapper is never reached. An
earlier version of this patch used it and the vector-env test caught the assert.
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
Four kinematic processor steps read the observation as
observation = self.transition.get(TransitionKey.OBSERVATION).copy()
if observation is None:
raise ValueError("Joints observation is require for computing robot kinematics")
so `.copy()` runs first and the guard below it is unreachable. A transition
without an observation raises `AttributeError: 'NoneType' object has no
attribute 'copy'` instead of the intended message.
That transition is not hypothetical: `RobotProcessorPipeline.process_action`
builds one with `create_transition(action=action)`, which sets
`TransitionKey.OBSERVATION` to None.
Reads the value first, checks it, then copies. Affects EEReferenceAndDelta,
InverseKinematicsEEToJoints, GripperVelocityToJoint and InverseKinematicsRLStep.
Adds a parametrised regression test covering all four; each fails with the
AttributeError if the fix is reverted.
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* feat(transforms): add 8 robotics-relevant image augmentations
Add GaussianNoise, MotionBlur, JPEGCompression, GaussianPatchBrightness,
RandomShadow, CoarseDropout, GammaCorrection, and PlanckianJitter.
Each transform addresses a real-world failure mode not covered by the
existing 6 defaults (sensor noise, motion blur, compression artifacts,
uneven lighting, cast shadows, partial occlusion, exposure variation,
color temperature shift).
All transforms are pure PyTorch, follow the make_params/transform
pattern, and integrate with ImageTransformConfig via a registry.
* add augmentation showcase image for PR
* update showcase with better sample frame
* tune showcase to balanced augmentation intensity
* tune showcase: softer shadow, dropout, jitter intensity
* refactor(transforms): several updates
* update image
* chore(media): remove example
* chore: add link to example
---------
Co-authored-by: Yuxian LI <liyuxian1358@gmail.com>
* Fix add_features for multi-dimensional per-frame features
* Use generic names in multidimensional add_features test
* tests(all shapes): enhancing tests to cover all possible features shapes
* chore(format): formatting code
---------
Co-authored-by: felixmin <felix.minze@live.de>
* Support task replacement mappings in modify_tasks
Part of #2326.
Signed-off-by: 陈伟 <woshei0a0a0a@qq.com>
* feat(task modification precedence): Improving task modification precedence so that all modes can be used in a single run. Adapting tests accordinginly.
* chore(fromat): formatting code
* docs(docstrings): updating docstrings
* docs(update): updating docs with the task modification features
---------
Signed-off-by: 陈伟 <woshei0a0a0a@qq.com>
Co-authored-by: vvezre <93599357+vvezre@users.noreply.github.com>
is_saving_step was the only step-frequency check without a `> 0` guard,
so save_freq=0 raised ZeroDivisionError from `step % 0` on the first
step. Route the decision through a should_save_checkpoint helper that
treats a non-positive save_freq as "save only the final checkpoint",
matching how log_freq/eval_freq handle non-positive values.
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
The discard-path fix (#3683) deletes staging for all camera_keys in
clear_episode_buffer(). The post-save cleanup in save_episode() must keep
iterating image_keys only: with batch_encoding_size > 1 the video staging
frames of already-saved episodes stay on disk until the batch encoder
consumes and deletes them. Add a regression test pinning that behavior,
plus a comment explaining why the two paths differ.
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix(datasets): stop frame errors being treated as shard exhaustion in StreamingLeRobotDataset
StreamingLeRobotDataset.__iter__ caught every RuntimeError and treated all as exhausted shard. Real errors like video decode failure made each shard get dropped on the first frame, so iteration ended while yeilding zero frames with no errors.
Shard exahustion is StopIteration raised from make_frame generator, which python converts to RuntimeError with StopIteration as __cause__. Added check to tell StopIteration from everything else, consuming real shard exhaustion while re-raising everything else.
Added a test that injects a decode failure and asserts iteration raises instead of returning on empty stream.
Fixes#4066
* refactor(datasets): exception streaming
---------
Co-authored-by: Mohit Yadav <mohitydv09@gmail.com>
serialize_torch_rng_state/deserialize_torch_rng_state only handled CPU
and CUDA generators. On MPS, resumed training was not bit-exact for any
stochastic op (dropout, ACT's CVAE noise) since the MPS generator's state
was never saved or restored. Mirrors the existing CUDA branch using
torch.mps.get_rng_state/set_rng_state (available since torch 2.11).
Note: get_rng_state()/set_rng_state() (used by seeded_context()) have the
same gap but are out of scope here — happy to follow up separately if
useful.
Co-authored-by: Sunny Dave <sunnydave@Sunnys-Mac-Studio.local>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix(utils): raise ValueError from get_safe_torch_device
Bare asserts vanish under python -O and look like programmer bugs.
Convert unavailable CUDA/MPS/XPU requests into clear ValueErrors.
* style: combine nested with in device util tests (ruff)
---------
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix(utils): reject zero-norm / invalid quaternions in Rotation
Zero or non-finite inputs previously slipped through and produced NaN
rotation matrices on later convert/apply. Validate shape and scept for
norm > 0 before normalizing.
* fix(teleop): degrade phone AR quat parse like missing pose
Address review on #3988: Rotation.from_quat now rejects zero/NaN
quaternions. Wrap HEBI iOS ARKit permission in ValueError and return the
existing (False, None, None, None) path so teleop does not die mid-session
before tracking is ready.
* style: ruff format long ValueError in rotation.py
---------
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix(robot): type FK-to-EE action features as ACTION not STATE
ForwardKinematicsJointsToEEAction.transform_features declared its
end-effector action features (ee.x/y/z/wx/wy/wz/gripper_pos) with
FeatureType.STATE, copied verbatim from the sibling
ForwardKinematicsJointsToEEObservation (where STATE is correct for
OBSERVATION features). Every other action-producing step in this file
(EEReferenceAndDelta, InverseKinematicsEEToJoints, InverseKinematicsRLStep)
types its ACTION-bucket features as FeatureType.ACTION.
The mismatch mis-classifies the converted EE actions as state, which
propagates a wrong feature schema to downstream consumers keyed on
FeatureType (e.g. normalization norm_map, policy input/output feature
classification).
* test(robot): FK-to-EE step feature-type contract (action vs observation)
Asserts ForwardKinematicsJointsToEEAction emits EE features in the ACTION
bucket typed FeatureType.ACTION, and ForwardKinematicsJointsToEEObservation
emits them in the OBSERVATION bucket typed FeatureType.STATE.
* chore: delete user file
* chore(processor): reduce verbosity
---------
Co-authored-by: Jaagat-P <jaagatp05@gmail.com>
* fix(evo1): re-pad normalizer stats when loading from checkpoint
reconcile_evo1_processors did not re-pad the (un)normalizer stats to
max_state_dim/max_action_dim on the checkpoint-load path. When
lerobot-train loads a checkpoint (e.g. stage2 from a stage1 checkpoint)
it injects the raw dataset stats via processor overrides, so LIBERO's
8-dim state stats normalized a 24-dim padded state and crashed with
"size of tensor a (24) must match tensor b (8)".
Restore _refresh_evo1_normalization_steps (removed in the "remove legacy
codepaths" refactor) and call it from reconcile_evo1_processors so the
loaded stats/features are re-padded to EVO1's fixed widths. Padding is a
no-op when stats are already at the target width.
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(evo1): cover reconcile re-padding of overridden normalizer stats
Regression test for the stage2-from-checkpoint crash: reloading a
checkpoint with raw (unpadded) dataset stats injected via processor
overrides must be re-padded to max_state_dim/max_action_dim by
reconcile_evo1_processors, otherwise normalizing the padded state
raises a shape mismatch.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Martino Russi <martino@huggingface.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix(robots): retry SO follower/leader bus reads on transient Feetech errors
SO-100/SO-101 teleoperation aborts when a sync_read of Present_Position
returns a corrupted status packet ("Incorrect status packet!"), which the
Feetech bus emits intermittently under load. The read path already supports
a num_retry argument but the SO follower and leader never used it, so a single
transient failure crashed the control loop.
Add a max_read_retry config option (default 3) to SOFollowerConfig and
SOLeaderConfig and forward it to every Present_Position sync_read. Retries are
immediate and only happen on failure, so the steady-state read cost is
unchanged; set max_read_retry=0 to restore the previous behavior.
Fixes#3131
* chore(robots): change defaults
---------
Co-authored-by: isaka1022 <isaka1022@gmail.com>
* fix(datasets): bound memory of augment_dataset_quantile_stats by sampling frames
Per-episode stats previously materialized every frame (and decoded up to 16
episodes in parallel), so peak memory scaled with episode length and OOM'd on
large datasets (#2889). Numeric features are now read in full from the table
(exact), while only image/video frames are sub-sampled per episode using the
existing sample_indices heuristic. Worker count is configurable via
LEROBOT_STATS_MAX_WORKERS; --no-sampling restores exact behavior.
* Update tests/datasets/test_augment_quantile_stats.py
Co-authored-by: Haoming Song <1847575517@qq.com>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
---------
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Haoming Song <1847575517@qq.com>
* feat(camera): add manual exposure, gain, and white balance options for RealSense cameras
The RealSense camera integration lacked sensor-level controls, causing
issues like unstable lighting from auto-exposure hunting. This adds
optional `exposure`, `gain`, and `white_balance` fields to
RealSenseCameraConfig that disable the corresponding auto modes and
apply fixed values when set.
* fix: support D405 stereo module for sensor options
D405 exposes color stream via "Stereo Module", not "RGB Camera".
Fall back to Stereo Module when RGB Camera is not found.
* test(camera): add unit tests + range-aware errors for RealSense sensor options
Address PR #3220 review:
- Wrap set_option calls; re-raise ValueError with option name, value,
and sensor.get_option_range() diagnostics on out-of-range values.
- Add unit tests for _get_color_sensor (RGB Camera, D405 Stereo Module
fallback, diagnostic error) and _configure_sensor_options (no-op,
all values, unsupported warns, partial config, out-of-range raise).
* fix(realsense): validate manual color controls
* refactor(camera): apply feedback
---------
Co-authored-by: Lev Kozlov <kozlov.l.a10@gmail.com>
* (depth image processing): excluding depth frames from the RGB to BGR image processing
* test(update): updating tests to include RGB/BGR conversion checks
* Make SO follower P coefficient configurable
* chore(test): minimize tests
* feat(robots): expose PID coeff in SO arms
---------
Co-authored-by: taivu1998 <46636857+taivu1998@users.noreply.github.com>
Pepijn