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Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning

Andy Zeng
Shuran Song
Stefan Welker
Alberto Rodriguez
IEEE International Conference on Intelligent Robots and Systems (IROS) (2018)


Skilled robotic manipulation benefits from complex synergies between non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free. In this work, we demonstrate that it is possible to discover and learn these synergies from scratch by combining visual affordance-based manipulation with model-free deep reinforcement learning. Our method is sample efficient and generalizes to novel objects and scenarios.