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feat(rollout): decouple policy deployment from data recording with new lerobot-rollout CLI (#3413)

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* feat(scripts): lerobot-rollout

* fix(rollout) require dataset in dagger + use duration too

* fix(docs): dagger num_episodes

* test(rollout): fix expectations

* fix(rollout): features check

* fix(rollout): device and task propagation + feature pos + warn fps + move rename_map config

* docs(rollout): edit rename_map instructions

* chore(rollout): multiple minor improvements

* chore(rollout): address coments + minor improvements

* fix(rollout): enable default

* fix(tests): default value RTCConfig

* fix(rollout): robot_observation_processor and notify_observation at policy frequency instead of interpolator rate

Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* fix(rollout): prevent relativeactions with sync inference engine

Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* fix(rollout): rtc reanchor to non normalized state

Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* fix(rollout): fixing the episode length to use hwc (#3469)

also reducing default length to 5 minutes

* feat(rollout): go back to initial position is now a config

* fix(rollout): properly propagating video_files_size_in_mb to lerobot_dataset (#3470)

* chore(rollout): note about dagger correction stage

* chore(docs): update comments and docstring

* fix(test): move rtc relative out of rollout module

* fix(rollout): address the review comments

---------

Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Maxime Ellerbach <maxime.ellerbach@huggingface.co>
This commit is contained in:
Steven Palma
2026-04-28 00:57:35 +02:00
committed by GitHub
parent 77352c495c
commit ca87ccd941
62 changed files with 5577 additions and 2847 deletions
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tests/test_cli_peft.py
-82
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@@ -24,10 +24,6 @@ def lerobot_train(args):
return run_command(cmd="lerobot-train", module="lerobot_train", args=args)
def lerobot_record(args):
return run_command(cmd="lerobot-record", module="lerobot_record", args=args)
def resolve_model_id_for_peft_training(policy_type):
"""PEFT training needs pretrained models, this finds the pretrained model of a policy type for PEFT training."""
if policy_type == "smolvla":
@@ -155,81 +151,3 @@ def test_peft_training_params_are_fewer(policy_type, tmp_path):
f"--output_dir={output_dir}",
]
)
class DummyRobot:
name = "dummy"
cameras = []
action_features = {"foo": 1.0, "bar": 2.0}
observation_features = {"obs1": 1.0, "obs2": 2.0}
is_connected = True
def connect(self, *args):
pass
def disconnect(self):
pass
def dummy_make_robot_from_config(*args, **kwargs):
return DummyRobot()
@pytest.mark.parametrize("policy_type", ["smolvla"])
@skip_if_package_missing("peft")
def test_peft_record_loads_policy(policy_type, tmp_path):
"""Train a policy with PEFT and attempt to load it with `lerobot-record`."""
from peft import PeftModel
output_dir = tmp_path / f"output_{policy_type}"
model_id = resolve_model_id_for_peft_training(policy_type)
lerobot_train(
[
f"--policy.path={model_id}",
"--policy.push_to_hub=false",
"--policy.input_features=null",
"--policy.output_features=null",
"--peft.method=LORA",
"--dataset.repo_id=lerobot/pusht",
"--dataset.episodes=[0, 1]",
"--steps=1",
f"--output_dir={output_dir}",
]
)
policy_dir = output_dir / "checkpoints" / "last" / "pretrained_model"
dataset_dir = tmp_path / "eval_pusht"
single_task = "move the table"
loaded_policy = None
def dummy_record_loop(*args, **kwargs):
nonlocal loaded_policy
if "dataset" not in kwargs:
return
dataset = kwargs["dataset"]
dataset.add_frame({"task": single_task})
loaded_policy = kwargs["policy"]
with (
patch("lerobot.scripts.lerobot_record.make_robot_from_config", dummy_make_robot_from_config),
# disable record loop since we're only interested in successful loading of the policy.
patch("lerobot.scripts.lerobot_record.record_loop", dummy_record_loop),
# disable speech output
patch("lerobot.utils.utils.say"),
):
lerobot_record(
[
f"--policy.path={policy_dir}",
"--robot.type=so101_follower",
"--robot.port=/dev/null",
"--dataset.repo_id=lerobot/eval_pusht",
f'--dataset.single_task="{single_task}"',
f"--dataset.root={dataset_dir}",
"--dataset.push_to_hub=false",
]
)
assert isinstance(loaded_policy, PeftModel)