Motivation
Existing robot locomotion solutions were excessively noisy and unstable on complex terrain. We aimed to explore how reinforcement learning could systematically improve robotic walking patterns, balancing noise reduction, efficiency, and terrain adaptability.
Objectives & Goals
Reduce the robot's acoustic noise significantly through refined reward design, while ensuring robust, adaptive locomotion capable of reliably handling varied terrain, including stair climbing.
Solution & Implementation
Using Nvidia's Isaac Lab simulator, PPO, and Rapid Motor Adaptation, we carefully designed reward functions focused explicitly on reducing foot-impact forces to minimize noise. Our training systematically addressed progressively difficult terrains, notably stairs, leveraging transfer learning and incremental training techniques to maintain stability.
Results & Achievements
Simulations showed approximately 75% noise reduction compared to the baseline, drastically enhancing quiet operation. The trained policies exhibited stable and adaptable performance with some problems only when climbing stairs head-on in the real world, demonstrating our approach's potential for real-world applications.
Learnings & Reflections
This project reinforced our practical knowledge of RL training methods, simulation realism, and nuanced reward engineering, significantly advancing our proficiency in robotic locomotion tasks.