Learning Category-Level Generalizable Object Manipulation Policy via Generative Adversarial Self-Imitation Learning from Demonstrations. (arXiv:2203.02107v2 [cs.RO] UPDATED)

Generalizable object manipulation skills are critical for intelligent and
multi-functional robots to work in real-world complex scenes. Despite the
recent progress in reinforcement learning, it is still very challenging to
learn a generalizable manipulation policy that can handle a category of
geometrically diverse articulated objects. In this work, we tackle this
category-level object manipulation policy learning problem via imitation
learning in a task-agnostic manner, where we assume no handcrafted dense
rewards but only a terminal reward. Given this novel and …

arxiv imitation learning policy