* feat(train): FSDP checkpoint saving
* adding docs for FSDP
* adding a test for the fsdp checkpoint path
* cleanup
* fixing final upload to hub
* refactored initial implementation to use torch fsdp api and adding new tests
- Guard ACT's KL divergence computation against None latent params to
prevent crashes during eval when use_vae is set but the forward path
returns no VAE outputs.
- Add offline batch fallback to Diffusion's predict_action_chunk() so
it works with dataloader batches (empty queues) in addition to the
existing online rollout path (populated queues). This enables batched
action prediction for offline evaluation.
- Add pretrained_revision field to PreTrainedConfig (policies) and
RewardModelConfig (reward models), and thread it through make_policy(),
make_pre_post_processors(), and make_reward_model() so that weights and
processor configs can be loaded from a specific Hub commit, branch, or
tag. Defaults to None (latest version, preserving current behavior).
Dataset and env hub loading already supported revision pinning.
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix(logging): batch wandb metrics
- Batch all metrics into a single wandb.log() call instead of one per
key, reducing API overhead.
- Add support for list-valued metrics by expanding them to indexed keys (e.g.
metric_0, metric_1).
* fix(stats): handle scalar stats robustly
- Wrap cast_stats_to_numpy with np.atleast_1d to prevent 0-d arrays
from scalar stats causing shape mismatches downstream.
* fix(logging): remove unused list-valued metric expansion
---------
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* feat(envs): add env plugin discovery
- Add 'lerobot_env_' to third-party plugin discovery prefixes, completing
the plugin system for all component types (robots, cameras, teleoperators,
policies, and now environments). External packages named lerobot_env_*
can self-register EnvConfig subclasses on import, enabling --env.type=
resolution without lerobot code changes.
* feat(envs): add generic observation passthrough
- Add generic observation passthrough in preprocess_observation() for
unhandled ndarray/tensor keys, replacing the pattern of adding per-env
hardcoded key handlers. Extra keys are forwarded as observation.<key>
and can be shaped by env-specific ProcessorSteps via get_env_processors().
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Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* Do not set stop_event to None when stopping thread
* fix(cameras): snapshot stop_event in read loops to avoid None deref
The background read loops accessed self.stop_event repeatedly while
_stop_read_thread() can reassign it to None after join(). Reading the
attribute across the loop condition (and a mid-loop re-check) was a
time-of-check/time-of-use race: stop_event could flip to None between
the `is None` test and the `.is_set()` call, raising AttributeError on
the worker thread.
Snapshot self.stop_event into a local once, guard it, and loop on the
local Event. The Event object is thread-safe and lives for the thread's
lifetime; _stop_read_thread() always calls .set() before nulling the
attribute, so the local observes the stop and exits cleanly. This also
lets us drop the redundant pre-lock stop check.
Applies to OpenCVCamera, RealSenseCamera, and ZMQ camera.
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Co-authored-by: Anes Benmerzoug <anes.benmerzoug@gmail.com>
CarolinePascal