Sample-Efficient Learning of Nonprehensile Manipulation Policies via Physics-Based Informed State Distributions

Understand

This paper proposes a sample-efficient yet simple approach to learning closed-loop policies for nonprehensile manipulation.

• Although reinforcement learning (RL) can learn closed-loop policies without requiring access to underlying physics models, it suffers from poor sample complexity on challenging tasks.
• To overcome this problem, we leverage rearrangement planning to provide an informative physics-based prior on the environment's optimal state-visitation distribution.
• Specifically, we present a new technique, Learning with Planned Episodic Resets (LeaPER), that resets the environment's state to one informed by the prior during the learning phase.