TABLEMIND — Bimanual VLA

TABLEMIND is a simulation-first Physical AI system for bimanual manipulation using two simulated SO-101 arms in MuJoCo. It interprets natural-language instructions together with simulated camera observations, maintains task context, plans multi-step actions, coordinates both manipulators, and verifies the resulting scene state. The system is designed around Hugging Face LeRobot and SmolVLA-compatible policy workflows, with a task-planning layer for shared-workspace reasoning, collision-aware sequencing, object hand-offs, complementary dual-arm actions, and recovery when the scene changes. TABLEMIND also includes environment randomization and evaluation tooling to measure robustness across different object placements and physical and visual conditions. For deployment optimization, the project integrates the Intel OpenVINO and OpenVINO Physical AI inference path, with support for model conversion, optimization, precision selection, device benchmarking, and Intel CPU/iGPU/NPU execution where supported. The final target is an Intel Core Ultra Series 2/3 system, while the robotic environment remains fully simulation-first as required by the online challenge. The project is built to be reproducible, measurable, and demonstrable through randomized MuJoCo evaluations, inference benchmarks, and a clear end-to-end perception-to-action workflow.