  In this work, we present an approach to planning for humanoid mobility.
Humanoid mobility is a challenging problem, as the configuration space for a
humanoid robot is intractably large, especially if the robot is capable of
performing many types of locomotion. For example, a humanoid robot may be able
to perform such tasks as bipedal walking, crawling, and climbing. Our approach
is to plan for all these tasks within a single search process. This allows the
search to reason about all the capabilities of the robot at any point, and to
derive the complete solution such that the plan is guaranteed to be feasible. A
key observation is that we often can roughly decompose a mobility task into a
sequence of smaller tasks, and focus planning efforts to reason over much
smaller search spaces. To this end, we leverage the results of a recently
developed framework for planning with adaptive dimensionality, and incorporate
the capabilities of available controllers directly into the planning process.
The resulting planner can also be run in an interleaved fashion alongside
execution so that time spent idle is much reduced.
