Subtitle: From Quadrupedal Locomotion to Humanoid Challenges
[Code Repository]
All the code for this tutorial is available on GitHub. Each chapter (from Chapter 2 onwards) has its own dedicated branch, so you can easily check out the completed code for any part of the tutorial.
- Objective: To validate the Isaac Lab installation and observe the performance of a pre-trained, built-in policy.
- Methodology: We will execute the default training and playback scripts provided with Isaac Lab for the Unitree Go2 quadruped. This establishes a performance baseline and familiarizes the user with the end-to-end workflow, from training initiation to policy evaluation.
- Objective: To establish a custom project structure and understand the fundamental development paradigms within Isaac Lab.
- Methodology: We will utilize the built-in template generator to create a new project. A comparative analysis of the Manager-Based and Direct workflows will be presented, justifying the selection of the Manager-Based paradigm for its modularity and scalability.
- Objective: To deconstruct the
ManagerBasedRLEnvCfg class to understand its primary components and their roles in defining an RL task.
- Methodology: A detailed examination of the key configuration objects within the generated
_env_cfg.py file: ObservationsCfg, CommandsCfg, EventsCfg, ActionsCfg, RewardsCfg, TerminationsCfg, and CurriculumCfg. We will modify simple parameters within the default cartpole example to observe their effects.
- Objective: To replace the default robot asset with the Unitree Go2 and prepare for the implementation of task-space control.
- Methodology: The environment configuration will be modified to spawn the Go2 asset instead of the cartpole. We will then perform a kinematic analysis of the robot's URDF file to extract the essential link lengths and joint axis parameters required for control.