Tutorial

Welcome to ioailab, the IOAI-oriented simulation platform built by the Galbot simulation team. This tutorial shows how to build a task step by step, starting from a simple PickCube task and ending with larger compound tasks that run through several phases.

The tutorial is split into three chapters. Read them in order: the first chapter teaches the basic task-generation loop, and the later chapters reuse that loop for component tasks and compound tasks.

simple task
-> component tasks
-> compound task

• Chapter 1: Build and Activate a Simple Task
• Chapter 2: Build PickToShelf From Component Tasks
• Chapter 3: Build SortToShelf With Task Options and Sequence Agents

The main workflow is iterative. A new task should become runnable before it becomes complete: first make IsaacLab construct the environment, then run one episode, refine the MDP and reset behavior, collect data, and only then add Mimic, training, evaluation, or compound-task structure. It is normal to return to an earlier step when a later step exposes a weak termination, observation, or initial-state definition.

Keep task MDP functions under semantic owners such as events.py, observations.py, rewards.py, and terminations.py. Do not add a generic terms.py bucket.

minimal task
-> single-env smoke
-> optional teleop
-> refine MDP/reset
-> multi-env smoke
-> collect data
-> optional mimic dataset expansion
-> train
-> evaluate
-> revisit task definition