Examples

Run GUI examples from make shell-gui; run headless data/training jobs from make shell. Each numbered example is a normal Python entry point.

Basic Pipeline

python examples/01_collect.py
python examples/02_mimic.py --task GalbotG1-PickCube-v0
python examples/03_train.py --task GalbotG1-PickCube-v0
python examples/04_eval.py --task GalbotG1-PickCube-v0 \
  --checkpoint outputs/pick_cube/model_best_training.pth --headless

01_collect.py shows the motion-planner path by default. It also contains commented blocks for TeleopAgent and final-scenario export. For GP001 teleop, use GalbotG1-PickCube-Teleop-v0 with TeleopAgent.from_device("gp001", task=task_id); rejected demos can be removed with dataset.drop() after an env.collect(...) candidate during done plus keep/drop/exit review.

Motion-planning collection is expert data generation. Expert tasks may have empty reward and curriculum managers because the planner, action stepping, and task termination define the episode boundary. Do not add dummy reward or curriculum terms only to produce manager summaries.

Component Tasks

Use examples/06_collect_component_task.py for standalone PickToShelf and SortToShelf phases. Select one COMPONENT_PRESET at the top of the file, then run the script.

PickToShelf presets target GalbotG1-PickToShelf-Pick-v0, GalbotG1-PickToShelf-Nav-v0, and GalbotG1-PickToShelf-Place-v0.

python examples/06_collect_component_task.py \
  --save-end-scenario data/pick_to_shelf/scenarios/nav_start.yaml

python examples/06_collect_component_task.py \
  --init-scenario data/pick_to_shelf/scenarios/nav_start.yaml \
  --save-end-scenario data/pick_to_shelf/scenarios/place_start.yaml

python examples/06_collect_component_task.py \
  --sorting-object red_cube \
  --save-end-scenario data/sort_to_shelf/scenarios/place_start_red_cube.yaml

GalbotG1-SortToShelf-Nav-v0 uses the task-local nav_sequence_agent: drive the base first, then set the place-start posture.

Coherent Tasks

Use examples/07_compound_task.py for full task flows. The default path uses task-owned phase agents; the file also shows how to override phase agents with planner or policy agents.

python examples/07_compound_task.py --task GalbotG1-PickToShelf-v0 --headless

python examples/07_compound_task.py --task GalbotG1-SortToShelf-v0 \
  --sorting-object red_cube --headless

python examples/07_compound_task.py --task GalbotG1-PickToShelf-v0 \
  --mode collect --dataset-path data/pick_to_shelf/full_expert.hdf5 --headless

Any BaseAgent can drive the same agent.act(env) -> env.step(action) loop: CuroboPlannerAgent, TeleopAgent, PolicyAgent, and TaskFlowAgent all return full IsaacLab action tensors.