Forging Physical AI on AMD ROCm. Our technical landscape, engineering practice, and roadmap live here.
When a Robot Arm Invents Its Own Grip: An RL Practice with OpenArm
总览回放 / Overview replay 📖 This is the concise version (~3 min). For the full engineering details (design decisions, algorithm / reward, diagnostics, reproduce commands), read the deep-dive → UniLab & Joint Release UniLab is a heterogeneous robot-RL training infrastructure: CPU-parallel physics simulation (MuJoCo / Motrix) and GPU policy learning are coupled through a unified runtime and shared memory — instead of pinning physics, rollout collection, and learning on a single GPU-resident simulation path. Tasks, rewards, and backend selection are expressed as Hydra owner YAMLs; training goes through a unified uv run train / uv run eval CLI covering PPO, SAC, TD3, APPO, and more. ...
Feeding the VLA: Generating Expert Grasp Trajectories for OpenArm on AMD ROCm
📖 This is the concise version (~3 min). For the full engineering details (design decisions, algorithm / reward, diagnostics, reproduce commands), read the deep-dive → Overview VLA (Vision-Language-Action) models are data-hungry, and real-robot collection is slow and expensive. So we flip it: on an AMD Instinct MI300X + ROCm box, we turn OpenArm’s pick-and-place into an in-sim expert trajectory data engine with openarm_mp_labs — given an object and a grasp pose, auto-solve a smooth, physically feasible, sub-mm-accurate demonstration trajectory that’s reproducible at scale. ...