Locomotion Strategy

Locomotion strategy research focuses on enabling robots, particularly legged robots, to move robustly and efficiently across diverse terrains and conditions. Current efforts concentrate on developing advanced control algorithms, often integrating reinforcement learning (RL) with model-based methods or central pattern generators (CPGs), to achieve adaptable and resilient locomotion. These approaches leverage various model architectures, including multi-modal RL, privileged learning, and hybrid control frameworks, to improve performance and generalization across different environments and robot morphologies. This research is crucial for advancing robotics in fields like search and rescue, exploration, and manufacturing, where robots need to navigate complex and unpredictable environments.