Reward Redistribution for Reinforcement Learning of Dynamic Nonprehensile Manipulation

Recent reinforcement learning (RL) systems can solve a wide variety of manipulation tasks even in real-world robotic implementations. However, in some nonprehensile manipulation tasks (e.g. poking, throwing), the classical reward system fails as the robot has to manipulate objects whose motion trajectory is partly uncontrollable. Such tasks require a specific type of reward that would reflect this...