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Projects: from simulation to real robot deployment

Choose a task and work through environment setup, training, control, and evaluation with a project, demo, or reproduction study. Follow a sequence of chapters to build a complete system, or choose a standalone experiment to test a method. For questions about principles, look up the related module in foundations. For original papers, data, and code, see the introduction's resources.

Where to start​

  • To build a system from scratch: start with the quadruped project. Its eight chapters cover actuators, kinematics, modeling, gaits, and policy training. Basic Python is required; you can study control and linear algebra alongside the experiments.
  • To learn about arm datasets and hardware operation: read the LeRobot course notes in Chinese, then move on to the SO-101 hardware tutorial. The notes cover Units 0–2 and require basic Python; the hardware tutorial requires a compatible robot.
  • To try a simulation first: choose MicroDuck RL or ACT bimanual manipulation, and set up the required environment.
  • To explore wheeled-biped balance control: read the Flamingo project preview for prerequisites and planned experiments.

Before starting, check the environment and goals. Keep your code, parameters, and results as you work, then use evaluation and failure cases to check your understanding. The project chapters are currently in Chinese; the status labels below describe the available source content.

Directions​

DirectionProjectsSuitable for
Robot arms5VLA, data collection, and imitation learning
Quadrupeds4Reinforcement learning, control, and sim-to-real
Bipeds and humanoids1 available, 2 plannedAdvanced control, reinforcement learning, and task planning
Mobile manipulation3Combining navigation and manipulation
Wheeled bipedsPreviewUnderactuated balance, Isaac Lab, and cross-simulator validation

Available projects​

AMD projects​

  • AUP Learning Cloud: use Ryzen AI APUs, JupyterHub, Code Server, and ROCm in your browser for exercises, edge inference, and small experiments.
  • MicroDuck RL · AMD ROCm: build ROCm Warp and MuJoCo Warp on a Radeon R9700, fix dynamic broadphase caching, and train biped PPO policies.
  • ACT bimanual training · AMD ROCm: train ACT in BF16 on Radeon GPUs, resume checkpoints, evaluate 20 episodes, and export videos.
  • Explore the Pupper quadruped: the AMD flagship project, with Pupper Locomotion for RL motion policies and Pupper VLA for vision-language-action intelligence.

Simulation projects​

ProjectTechnical pathStatus
Build a quadruped from scratchMuJoCo, PD control, kinematics, gaits, policy training, and perceptionOverview and 8 chapters available
MicroDuck RL: walking, standing up, and rollingmjlab, MuJoCo Warp, parallel PPO on CUDA, and 18 motion modesAvailable
Getting started with MuJoCoMJCF, physics simulation, and Python controlAvailable
DDPG InvertedPendulumContinuous-control basics and a DDPG baselineAvailable
DDPG ReacherTarget tracking with a planar robot armAvailable
DDPG PusherContact manipulation and reward designAvailable
ACT bimanual trainingALOHA, imitation learning, ACT, and evaluation over multiple episodesAvailable
π₀.₅ + RECAP: LIBERO reproductionValue models, advantage annotation, ACP, and LIBERO evaluationAvailable
Sim2Sim validationCross-simulator policy validationIn development
Flamingo wheeled biped · Isaac LabBalance, reinforcement learning, and cross-simulator validationCourse preview

Real robot projects​

The LeRobot notes introduce data and toolchains; the SO-101 project then applies that knowledge to a real robot arm.

ProjectTechnical pathStatus
LeRobot course notes in ChineseRobot learning, datasets, toolchains, and classical roboticsUnits 0–2 compiled
SO-101 + LeRobot hardware tutorialHardware connectivity, safety tests, and action playbackAvailable
ROS2 arm controlROS2 control and arm executionIn development
Sim2Real guideDeploying simulation policies and validating on hardwareIn development