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Generalizable object manipulation skills are critical for intelligent andmulti-functional robots to work in real-world complex scenes. Despite therecent progress in reinforcement learning, it is still very challenging tolearn a generalizable manipulation policy that can handle a category ofgeometrically diverse articulated objects. In this work, we tackle thiscategory-level object manipulation policy learning problem via imitationlearning in a task-agnostic manner, where we assume no handcrafted denserewards but only a terminal reward. Given this novel and challenginggeneralizable policy learning problem, we identify several key issues that canfail the previous imitation learning algorithms and hinder the generalizationto unseen instances. We then propose several general but critical techniques,including generative adversarial self-imitation learning from demonstrations,progressive growing of discriminator, and instance-balancing for expert buffer,that accurately pinpoints and tackles these issues and can benefitcategory-level manipulation policy learning regardless of the tasks. Ourexperiments on ManiSkill benchmarks demonstrate a remarkable improvement on alltasks and our ablation studies further validate the contribution of eachproposed technique.

Learning Category-Level Generalizable Object Manipulation Policy via Generative Adversarial Self-Imitation Learning from Demonstrations