RoboProof: Motion-Learning Lab

RoboProof is a physical-AI research prototype for improving how a VEX V5 robot moves. Instead of asking a language model to control motors directly, it combines a Guiding Vector Field (GVF), which provides a structured path-following reference, with a TD3 reinforcement-learning policy that learns residual adjustments from simulated sensor feedback. The system uses the project’s original Simulator/iraLIB so candidate policies can be compared with the existing controller and an untrained policy under repeatable conditions. Our primary learned candidate is a 512-wide, 10-hidden-layer network. We are training it on an AMD Instinct MI300X and preserving run data, checkpoints, and evaluation evidence. The goal is to determine whether learning improves movement reliability and efficiency—not to assume that a larger network or more GPU utilization guarantees better results. RoboProof is still experimental. Training is underway, and the learned policy has not yet passed the independent qualification gate. We are looking for robotics, controls, reinforcement-learning, and simulator contributors to help valid