In this workshop we aim to answer the following research questions:

• Can we go beyond imitation of animals' locomotion, and use other biological insights, like action-perception loop, to develop better locomotion learning frameworks?

• Can multimodal active perception improve the robot's agility, learning performance, or robustness?

• How important is contact sensing for locomotion? Should we exploit contacts rather than avoid them? 

• How important is it to perceive terrain properties during locomotion? Can we adapt locomotion to deal with different terrains? How can we simulate terrain?

• Can we learn directly on real platforms? Do we need safety techniques to learn in the real world?

• How can we learn to switch between different gaits using perception, e.g., from walking in the mud to swimming? 

• How to exploit these complex locomotion skills and advanced perception to solve long-term or high-level navigation tasks?

• How can we leverage the foundational models for improving multimodal and active perception for locomotion? Are foundational models an answer to all the questions above?

We aim to answer these research questions by combining the expertise of a wide range of diverse researchers with different expertise levels, scientific backgrounds, and scientific areas. 

We believe that innovative research can stem from the combination of insights from established researchers and novel ideas from early-career students and postdocs. 

Also, we believe that having a highly interdisciplinary approach, combining various fields around the common problem of locomotion learning, can lead to agile and intelligent locomotion platforms. 

Our key theme will be learning: we believe that learning techniques are key to the success of robots in the real world and that without learning modules (for control, active perception, and high-level reasoning) we cannot achieve robust dynamic locomotion rivaling the agility, dexterity and versatility of animals.