Machine learning, particularly Reinforcement Learning, has revolutionized the development of advanced locomotion controllers. These advancements have enabled legged robots not only to locomote on rough terrain but also navigate in complex environments with remarkable agility.“Robot Parkour” is one of these achievements—robots can now jump on boxes, cross narrow passages, overcome large gaps, or crawl beneath low barriers.

While the agility of robots improves at a fast pace, we are still far from the robustness and versatility we can find in nature. Animals leverage their morphology and senses with their capacity to adapt in the wild to achieve extraordinary locomotion capabilities. For robots to be able to achieve this level of performance, we need a comprehensive view that encompasses what we observe on animals, where perception, action, and learning are tightly coupled.

In this workshop, we will discuss the future of locomotion learning research from this holistic perspective. To achieve this, we will gather top researchers in locomotion learning (LL), bioinspiration (BIO), and multi-modal and active perception (MAP). Our objective is to understand how these diverse disciplines talk to each other and converge toward the development of agile, robust, and safe locomotion algorithms.