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ioailab

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

make shell-gui
python examples/01_collect.py

Runtime boundaryimport ioailab is side-effect free. Task IDs are registered only through explicit imports under ioailab.tasks; IsaacLab owns app launch, env construction, managers, sensors, PhysX, and stepping.

Rendered scenes

Stack cube

The loop

Every workflow is the same explicit loop: build an env, build an agent, step, and records data through env.collect(...).

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",
)

CuroboPlannerAgent, PolicyAgent, TeleopAgent, and TaskFlowAgent are interchangeable at this boundary. env.collect(...) and env.evaluate(...) remain convenience helpers for batch data generation and metrics.

Current surface

AreaSurface
Robot cfgsG1 articulation, arm/leg/gripper/base actions, camera activation, tensor helpers
TasksGalbotG1-PickCube-v0, -StackCube-v0, -Reach-v0, -BaseNav-v0, -PickToShelf-v0, -PickToShelf-Pick/Nav/Place-v0, -SortToShelf-v0, plus teleop/Mimic/policy variants
Data pathHDF5 collection, Mimic expansion, robomimic Diffusion Policy training, evaluation
TeleopGP001 left-wrist/front-head RGB collection with keep/drop/exit review
PlanningcuRobo v2 (curobov2) agents that emit IsaacLab action tensors

Where to go next

NeedPage
Build a task end-to-endTutorial
Run the numbered examplesExamples
Understand the boundaryArchitecture
Compose agents and task flowsAction Agents & Task Flows
Look up task IDsTasks
Collect, Mimic, train, exportData & Datasets
Configure camerasSensors
Joint helpers and assetsRobot Reference
Work inside DockerDeveloper Workflow

AGENTS.md and Architecture are the design source of truth.