Efficient Humanoid Motion Planning with Realtime End-Pose Selection in Complex Environments

In this talk, Yiming will introduce a novel inverse Dynamic Reachability Map (iDRM) that allows a floating base robot system to find valid and sufficient end-poses in complex and changing environments in real-time. End-pose planning, i.e. finding valid stance locations and collision-free reaching configurations, is an essential problem in humanoid applications, such as providing goal states for walking and motion planners. However, it is non-trivial in complex environments, where standing locations and reaching postures are restricted by obstacles. Our proposed approach, iDRM, customizes the robot-to-workspace occupation list and uses an online update algorithm to enable efficient reconstruction of the reachability map to guarantee that the selected end-poses are always collision-free.