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A dynamic-world representation for Physical AI Physical AI is not only about generating pixels; it is about sensing, reconstructing, and querying a changing real world. Conventional 4DGS commonly starts from synchronized, calibrated multi-view video. This article explores a lower-capture-barrier path: start from monocular video, synthesize synchronized multi-view observations with a 4D video-generation model, and build a queryable 4DGS representation of the dynamic world. Within rocPAI-Forge’s Physical AI direction, this pipeline is a dynamic visual layer: ...

📖 This is the concise version (~3 min). For the full engineering details (design decisions, algorithm / reward, diagnostics, reproduce commands), read the deep-dive → From “can record” to “can train & eval” The intro post wired SO-101, MuJoCo, and LeRobot on AMD ROCm so you can teleoperate in sim and collect expert trajectories. v0.1.3 (release-v0.1.3) closes the next loop — Lab 01 pick-and-place: sim demos → ACT / SmolVLA training → MuJoCo closed-loop eval, plus downloadable Hub reference assets. ...

SO-101 SimStudio 项目介绍 / Project intro 📖 This is the concise version (~3 min). For the full engineering details (design decisions, algorithm / reward, diagnostics, reproduce commands), read the deep-dive → Why this project Like many in this community, I started my physical-AI and robotics journey with SO-101 and LeRobot — an excellent on-ramp: open hardware, an active community, and a clear dataset format that gets you moving quickly. ...

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. ...

📖 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. ...