A Single-Planner Approach to Multi-Modal Humanoid Mobility

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.

Authors

• Andrew Dornbush
• Karthik Vijayakumar
• Sameer Bardapurkar
• Fahad Islam 0002
• Masayuki Ito
• Maxim Likhachev

Keywords

• Planning
• Task analysis
• Aerospace electronics
• Legged locomotion
• Superluminescent diodes
• Humanoid robots
• Search Space
• Configuration Space
• Humanoid Robot
• Single Search
• Robot Capabilities
• Mobility Tasks
• Bipedal Walking
• Regional State
• State Space
• Search Algorithm
• Grid Search
• Low-dimensional Space
• Planning Phase
• End-effector
• Low-dimensional Representation
• Adaptive Framework
• Planning Time
• Multiple Representations
• Search Phase
• Adaptive Search
• Robot Pose
• Execution Path
• Locomotion Mode
• Phase Tracking
• Motion Primitives
• High-dimensional State Space
• 1st Phase
• 2nd Phase
• Field Test
• Degrees Of Freedom