Bimanual Pick-Handoff-Place with VLM Planning

Two simulated SO-101 arms face each other across a table. Arm A picks up a cup, hands it to Arm B across a shared reach zone, and Arm B places it on a marked target — all driven by a natural-language instruction that a hosted vision-language model (Gemini 3.6 Flash) interprets alongside a rendered camera frame to select which scripted primitive to run next. A local CLIP model, converted to OpenVINO IR and benchmarked on Intel CPU, cross-checks that the target object is actually visible in the scene, giving the submission a genuine local-inference component alongside the hosted VLM. All arm motion uses classical inverse kinematics and waypoint control rather than a learned policy — a deliberate scope decision made under a tight timeline, documented openly in the repo's README. The system was evaluated across 10 randomized seeds (varying cup position, color, and lighting) with a 100% task success rate. Several episodes exercised the planner's fallback path after hitting the VLM provider's free-tier rate limit, demonstrating graceful degradation rather than failure. The OpenVINO benchmark shows FP16 CLIP inference running about 11% faster than FP32 on the available hardware, with half the model size. As an online-track team, we benchmarked on our own available Intel Core i7 hardware rather than Core Ultra Series 2/3, which wasn't provided to remote participants — this and every other simplification (kinematic grasping, position-only IK, narrow randomization) is documented transparently in the repository rather than glossed over.