Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
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 through model-free deep reinforcement learning... (read more)
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Methods used in the Paper
| METHOD | TYPE | |
|---|---|---|
Q-Learning
|
Off-Policy TD Control |