TableGuard-Lite: Safe Bimanual VLA Control

TableGuard-Lite is a safety-aware closed-loop Vision-Language-Action (VLA) system for bimanual robotic table setting. It combines natural-language instructions, three RGB camera views, and a 12-joint robot state to generate coordinated actions for two SO-101 robot arms using SmolVLA. Instead of executing long open-loop trajectories, the system predicts short action chunks, executes only a small portion, and then replans from fresh observations. A runtime safety supervisor checks joint-rate limits, unexpected contacts, collisions, and scene preservation, stopping unsafe behavior before it can propagate. TableGuard-Lite also introduces recovery-oriented learning: states encountered during failed closed-loop execution are captured and reused for targeted fine-tuning. The project includes a successful full reference manipulation sequence, learned closed-loop control experiments, failure-driven recovery training, three-camera perception, and safety-supervised execution. OpenVINO export, numerical-parity verification, and Intel CPU inference were also successfully demonstrated. TableGuard-Lite shows how Physical AI systems can combine VLA intelligence with safety, recovery, and deployment verification for more reliable real-world robotic manipulation.