EchoGrasp — The Robot That Knows When It Failed

EchoGrasp is a voice-directed bimanual robot-arm system built for the Intel Robotics track, stacked with the Speechmatics Bonus Award. Most robotics demos show a robot that works. EchoGrasp shows one that knows when it didn't, and fixes itself. You speak a command; Speechmatics transcribes it live; an LLM (via Groq) turns it into a strict, typed action plan; two simulated Franka Panda arms (PyBullet) execute it step by step; and after every placement, a real Intel OpenVINO INT8 model inspects a camera frame to judge whether the object actually landed where intended. When a step fails, the system says so in plain language ("gripper closed on nothing") and automatically re-plans with a corrected approach before retrying, up to a bounded limit. A live Honesty Dashboard shows the transcript, plan, per-step status, latency, and a running session success rate in real time. We're upfront that the OpenVINO model, trained on ~900 synthetic frames, measures roughly 60-70% accuracy alone, so the pass/fail call is confirmed against the simulator's own ground truth rather than the model's raw output. That model still runs genuine inference every placement step, and its confidence is what's reported live. In real warehouses, the expensive failures are the silent ones a system never notices; EchoGrasp is a small, working demonstration of catching those before they become someone else's problem.