Bimanual SO-101: LLM-Planned Table Setting

We built an end-to-end perception-to-action pipeline for the Intel Bimanual VLA challenge: two SO-101 arms in MuJoCo open a drawer, retrieve a spoon and fork, hand the spoon from one arm to the other, place the plate and cutlery, and set the mug. Pipeline: (1) Understand - Qwen2.5-1.5B-Instruct quantized to INT4 and served with OpenVINO GenAI turns the instruction into a structured plan (items, arm assignment, handedness). A left-handed request mirrors the layout and changes which arm moves the mug. (2) Observe - an overhead camera feeds a small U-Net segmentation net exported to OpenVINO INT8 (about 2.3 ms per frame on CPU); object poses come from the camera, not simulator state. (3) Act - damped-least-squares IK skills coordinate both arms; the hand-off goes through a shared staging zone because two top-down SO-101 grippers cannot meet within 12 cm. Robustness: every seed randomizes object mass, friction, size, lighting, table colour and placement. Results: 10/10 tuning seeds, 10/10 hold-out seeds, 10/10 left-handed layout, all camera-driven and LLM-planned. The demo video shows the command, seed parameters, per-step placement error and final state for all 10 seeds. Honest scope: the planner and perception net are learned models; manipulation skills are scripted. Benchmarks so far are on a cloud Intel-compatible CPU (planner 2.8 s per plan, MuJoCo 20k steps/s); the bench script reports latency, throughput, device and precision per model and is ready to run on Core Ultra CPU/iGPU/NPU.