When a Robot Arm Invents Its Own Grip: An RL Practice with OpenArm

Mon, 06 Jul 2026 00:00:00 +0000

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

Tue, 30 Jun 2026 00:00:00 +0000

📖 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.

Posts on rocPAI-Forge

When a Robot Arm Invents Its Own Grip: An RL Practice with OpenArm

UniLab & Joint Release

Feeding the VLA: Generating Expert Grasp Trajectories for OpenArm on AMD ROCm

Overview