Locomotion Policy

Locomotion policy research focuses on developing control algorithms that enable robots, particularly legged robots, to move effectively and robustly in diverse environments. Current research emphasizes learning-based approaches, often employing reinforcement learning (RL) with various architectures like Central Pattern Generators (CPGs) and model predictive control (MPC), sometimes integrated with deep learning for improved stability and safety guarantees. This field is crucial for advancing robotics, enabling more agile and adaptable robots for applications ranging from search and rescue to industrial automation and even assisting humans in challenging terrains.