Designing Robust State and Reward Systems for Robotic Reinforcement Learning

Building effective reinforcement learning for robotics comes down to two crucial elements: what your robot perceives (states) and what motivates it (rewards). Through our PCB testing arm project, I’ve found that carefully limiting state information actually improves learning, while balanced reward functions prevent unexpected behaviors. This practical guide breaks down how to design these components for embedded systems and avoid common pitfalls that waste development time. The principles here apply across robotics applications and can significantly reduce training time while improving real-world performance.

March 30, 2025 6 minutes