MDP-based Motion Planning for Grasping in Dynamic Scenarios

Path planning for robotic manipulation is a well understood topic as long as the execution of the plan takes place in a static scene. Unfortunately, for applications involving human interaction partners a dynamic obstacle configuration has to be considered. Furthermore, if it comes to grasping objects from a human hand, there is not a single goal position and the optimal grasping configuration may change during the execution of the grasp movement. This makes a continuous re-planning in a loop necessary. Besides efficiency and security concerns, such periodic planning raises the additional requirement of consistency, which is hard to achieve with traditional sampling based planners. We present an online capable planner for continuous control of a robotic grasp task. The planner additionally is able to resolve multiple possible grasp poses and additional goal functions by applying an MDP-like optimization of future rewards. Furthermore, we present a heuristic for setting edges in a probabilistic roadmap graph that improves the connectivity and keeps edge count low.