Training a Robotic Arm Using MuJoCo and JAX on AMD Hardware with ROCm™

2026-03-31 · Source: AMD ROCm Blogs · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

This article details the process of training a UFactory X-Arm 7 robotic arm to perform a pick-and-place task using reinforcement learning (RL) within the MuJoCo simulator, accelerated by JAX on AMD GPUs with ROCm 7.2. It covers the full pipeline, including environment setup, creating custom robot and scene descriptions in MJCF, and configuring the training code. Key aspects include multi-phase reward shaping to guide the policy through approach, grasp, and lift stages, and implementing domain randomization to improve generalization to real-world variability. The guide provides specific installation prerequisites for Linux (Ubuntu 24.04), ROCm environment variables, PPO hyperparameters, and modifications to the training script for JIT compilation caching and adaptive KL learning rates, culminating in instructions for launching and fine-tuning the training process.

Key takeaway

For Machine Learning Engineers developing robotics control policies, this guide provides a reproducible framework for training complex manipulation tasks on AMD hardware. You should adopt multi-phase reward shaping and domain randomization to enhance policy learning stability and real-world transferability. Consider fine-tuning with a reduced learning rate and tighter KL target for optimal performance after initial training.

Key insights

Reinforcement learning with MuJoCo and JAX on AMD ROCm enables robust robotic arm training for complex manipulation tasks.

Principles

• Phase-gated rewards decompose complex tasks into incremental sub-goals.
• Domain randomization improves policy robustness to real-world variability.
• Jacobian-based arm adjustments avoid costly IK during training.

Method

Train an X-Arm 7 for pick-and-place using MuJoCo, JAX, and ROCm. Configure a multi-phase reward function, implement domain randomization for physics parameters, and use an adaptive KL learning rate for stable policy updates.

In practice

• Use `MUJOCO_GL=osmesa` for headless MuJoCo rendering.
• Decimate high-polygon meshes to improve collision detection.
• Set `XLA_PYTHON_CLIENT_PREALLOCATE=false` for JAX memory management.

Topics

• Reinforcement Learning
• MuJoCo Simulation
• JAX on ROCm
• Robotic Pick-and-Place
• Domain Randomization

Code references

• jax-ml/jax
• google-deepmind/mujoco_playground
• google-deepmind/mujoco_menagerie
• jax-ml/jax
• google-deepmind/mujoco_menagerie

Best for: Robotics Engineer, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AMD ROCm Blogs.