Parkour Skill

Parkour skill in robotics focuses on enabling robots, particularly legged robots, to navigate complex, dynamic environments with agility and precision, mirroring the human ability to perform parkour. Current research heavily utilizes reinforcement learning, often incorporating techniques like constrained RL, implicit-explicit learning, and pretraining-finetuning frameworks, to train robots to perform various parkour maneuvers such as jumping, leaping, and climbing, often from visual input alone. This research is significant for advancing legged robot locomotion capabilities and has implications for applications in search and rescue, exploration, and other challenging environments. The development of robust, generalizable parkour skills in robots also contributes to a deeper understanding of dynamic locomotion and control.