Potential-Field Action Representation for Reinforcement Learning in Contact-Rich Manipulation
PA-RL, a reinforcement-learning framework that uses artificial potential fields as the action representation, is proposed and is the only method to reach a 100% evaluation success rate within the allotted training time, while the best baseline reaches 92.6%.
Xin-Yu Liu, Gokhan Solak, Arash Ajoudani
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