ioailab

ioailab provides G1 robot cfgs, an IsaacLab-style task registry, action/sensor helpers, dataset refs, and action agents for IsaacLab.

Quick Start

Build once and enter the GUI container (also the GP001 teleop shell):

make build
make shell-gui

make_env(...) handles app launch, task registration, and env construction in one call. Agents return full action tensors, and env.collect(...) records data through IsaacLab's recorder manager:

from ioailab.agents import CuroboPlannerAgent
from ioailab.envs import make_env

task_id = "GalbotG1-PickCube-v0"
env = make_env(task_id, num_envs=1)
agent = CuroboPlannerAgent.from_task(task_id)

dataset = env.collect(
    agent=agent,
    episodes=1,
    path="data/pick_cube_demos.hdf5",
)
env.close()

env.collect(...) exports data only when the env reports terminated/truncated or when the caller's max_steps limit is reached. env.is_running() is only the Isaac app-lifecycle guard.

The data pipeline (collect → Mimic → train → evaluate) runs as separate processes so IsaacSim state never leaks across stages:

python examples/01_collect.py   # collect motion-planner data; teleop is shown in comments
python examples/02_mimic.py     # mimic(dataset, episodes=...) expansion
python examples/03_train.py     # train robomimic_diffusion with RobomimicDiffusionTrainCfg
python examples/04_eval.py --checkpoint outputs/pick_cube/model_best_training.pth --headless

Use examples/06_collect_component_task.py for PickToShelf/SortToShelf component-task data, and examples/07_compound_task.py for coherent task runs. For motion-planning examples, use cuRobo v2 (curobov2). Registered task IDs and the rest of the workflow live in the docs below.

Traditional Vision Baseline

YOLO and FoundationPose workflows live under examples/vision_baseline/. See YOLO setup; FoundationPose setup is documented in the vision-baseline script headers.

📖 Documentation

Detailed documentation can be found at:

Online Documentation

Topic	Page
Tutorial	docs/tutorial.md
Examples	docs/examples.md
Architecture	docs/architecture.md
Action agents & task flows	docs/agents.md
Tasks	docs/tasks.md
Data & datasets (Mimic, LeRobot v3)	docs/data.md
Sensors and cameras	docs/galbot_sensors.md
Robot reference (joints, assets)	docs/reference.md
Development workflow	docs/development.md

Architecture-sensitive work should follow AGENTS.md and docs/architecture.md.