Towards Learning Abstract Representations for Locomotion Planning in High-dimensional State Spaces

Understand

Ground robots which are able to navigate a variety of terrains are needed in many domains.

• One of the key aspects is the capability to adapt to the ground structure, which can be realized through movable body parts coming along with additional degrees of freedom (DoF).
• However, planning respective locomotion is challenging since suitable representations result in large state spaces.
• Employing an additional abstract representation---which is coarser, lower-dimensional, and semantically enriched---can support the planning.