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The learning efficiency and generalization ability of an intelligent agentcan be greatly improved by utilizing a useful set of skills. However, thedesign of robot skills can often be intractable in real-world applications dueto the prohibitive amount of effort and expertise that it requires. In thiswork, we introduce Skill Learning In Diversified Environments (SLIDE), a methodto discover generalizable skills via automated generation of a diverse set oftasks. As opposed to prior work on unsupervised discovery of skills whichincentivizes the skills to produce different outcomes in the same environment,our method pairs each skill with a unique task produced by a trainable taskgenerator. To encourage generalizable skills to emerge, our method trains eachskill to specialize in the paired task and maximizes the diversity of thegenerated tasks. A task discriminator defined on the robot behaviors in thegenerated tasks is jointly trained to estimate the evidence lower bound of thediversity objective. The learned skills can then be composed in a hierarchicalreinforcement learning algorithm to solve unseen target tasks. We demonstratethat the proposed method can effectively learn a variety of robot skills in twotabletop manipulation domains. Our results suggest that the learned skills caneffectively improve the robot's performance in various unseen target taskscompared to existing reinforcement learning and skill learning methods.

Discovering Generalizable Skills via Automated Generation of Diverse Tasks