rocPAI-Forge is dedicated to forging Physical AI on AMD ROCm. We organize our work as a closed simulation ↔ real-world loop, with every stage running on the ROCm acceleration stack.

Solution Architecture

flowchart TB
    subgraph REAL["Real World"]
        RR["Real robots / arms / mobile platforms"]
        SEN["Sensor data capture"]
    end

    subgraph SIM["Simulation"]
        ENG["Sim engines
MuJoCo / Genesis, etc."]
        TWIN["Digital twins / scenes"]
    end

    subgraph LEARN["Learning"]
        RL["Reinforcement Learning
locomotion / manipulation"]
        WM["World Models"]
        VLA["VLA models
Vision-Language-Action"]
    end

    ASSET["3D assets & scene reconstruction
meshes / environments / twins"]

    SEN -- "Real2Sim: reconstruct dynamics/scenes" --> ASSET
    ASSET --> TWIN
    TWIN --> ENG

    ENG --> RL
    ENG --> WM
    ENG --> VLA
    RL --> POLICY["Policies / models"]
    WM --> POLICY
    VLA --> POLICY

    ENG -. "Sim2Sim: cross-simulator validation/transfer" .-> ENG

    POLICY -- "Sim2Real: deploy" --> INF["Real-robot inference
low-latency ROCm deploy"]
    INF --> RR
    RR --> SEN

    ROCM["AMD ROCm acceleration (train / sim / inference)"]
    ROCM --- SIM
    ROCM --- LEARN
    ROCM --- INF

Focus Areas

AreaWhat We Explore
Sim2RealClosing the gap between simulation and real robots — domain randomization, calibration, and deployment
Sim2SimCross-simulator validation and transfer (e.g. MuJoCo ↔ Genesis, etc.) for robust policies
Real2SimReconstructing scenes and dynamics from real data to improve simulation fidelity
3D Assets & Scene ReconstructionMeshes, environments, and digital twins for robotics and RL
Reinforcement LearningLocomotion, manipulation, and task-specific RL on ROCm-accelerated stacks
World ModelsPredictive models of environment dynamics for planning and control
VLA ModelsVision–Language–Action models for generalist robot policies
Real Robot InferenceLow-latency deployment on manipulators and mobile platforms with ROCm

Principles

  • Open by default — code, configs, and learnings shared with the community
  • ROCm-first — optimize and validate on AMD hardware and software stack
  • End-to-end — from data and sim to train, eval, and real-world inference
  • Evidence over hype — reproducible benchmarks, clear contracts, and honest trade-offs