Bimanual VLA Dinner Table Manipulation

Bimanual VLA Dinner Table Manipulation is a simulation-first Physical AI system developed for the Intel Physical AI Online Challenge. The project addresses the challenge of autonomously setting up a dinner table using two coordinated SO-101 robotic arms in a MuJoCo environment. The system combines natural-language instructions with camera-based scene observations to understand the task, identify relevant objects, maintain task state, plan manipulation sequences, and coordinate both robotic arms. The target workflow includes opening a drawer, retrieving utensils such as spoons and forks, transporting and placing a plate and cup, and verifying the resulting table configuration. A hierarchical architecture connects multimodal perception and language understanding with task-state management, task planning, bimanual action planning, robot control, simulation, and task verification. The system is designed to support collision-aware sequencing, object-specific grasping, placement verification, recovery from failed actions, and randomized initial conditions. MuJoCo provides the reproducible simulation environment, while LeRobot-compatible VLA policies are planned as the learned-policy layer for language-conditioned manipulation. The project also targets robustness evaluation across randomized seeds and variations in object placement and physical conditions. For Intel edge deployment, the project is designed around OpenVINO optimization and inference on Intel Core Ultra platforms, with latency, throughput, precision, and device benchmarking included in the final evaluation plan. The overall goal is a reproducible end-to-end Physical AI pipeline demonstrating multimodal reasoning, coordinated bimanual manipulation, robustness, and efficient edge inference.