X2 Sim + MC Deployment
Note: This document targets x86 host-side simulation, driving the MuJoCo simulation
sim_mujocowith the motion-control modulemc; robot modellx2501_3_t2d5(X2 T2.5). Together with RL Deployment these are two parallel simulation pipelines — pick one; do not drive the same simulation at the same time.
sim_mujoco: MuJoCo-based physics simulation acting as the robot body (HAL layer) — subscribes to joint commands and publishes joint and IMU statesmc: motion-control module, identical to the real robot, with built-in skills (standing / walking / getting up / animations, etc.) — receives high-level commands and outputs joint commandsDrive the robot by sending high-level commands via the SDK examples (
py_examples/examples); suitable for competition simulation and host-side algorithm integration.
Note
When to use this chapter: no reinforcement learning involved — drive the MuJoCo simulation with the official motion-control module mc (the same one used on the real robot) and validate your own high-level control logic on an x86 host. The skills (stand / walk / get-up / animation / upper body) are provided by mc, so you do not need to train a policy yourself.
If you only want to wire an existing onnx model into the SDK, see X2 RL Deployment.
- 1 X2 MuJoCo Motion-Control Simulation
- 1.1 Contents
- 1.2 Software Components
- 1.3 Prerequisites
- 1.4 Prepare the Software Directory
- 1.5 Option 1: Local Deployment
- 1.6 Option 2: Docker Deployment
- 1.7 Verify Motion Control
- 1.8 Stop the Environment
- 1.9 Troubleshooting