Learning Generalizable Skills via Automated Generation of Diverse Tasks


The learning efficiency of an intelligent agent can be greatly improved by utilizing a useful set of skills. However, the design of robot skills can often be intractable in real-world applications due to the prohibitive amount of effort and expertise that it requires. In this work, we introduce Skill Learning In Diversified Environments (SLIDE), a method to discover generalizable skills via automated generation of a diverse set of tasks. As opposed to prior work on unsupervised discovery of skills which incentivizes the skills to produce different outcomes in the same environment, our method pairs each skill with a unique task produced by a trainable task generator. To encourage generalizable skills to emerge, our method trains each skill to specialize in the paired task and maximizes the diversity of the generated tasks. A task discriminator defined on the robot behaviors in the generated tasks is jointly trained to estimate the evidence lower bound of the diversity objective. The learned skills can then be composed in a hierarchical reinforcement learning algorithm to solve unseen target tasks. We demonstrate that the proposed method can effectively learn a variety of robot skills in two tabletop manipulation domains. Our results suggest that the learned skills can effectively improve the robot's performance in various unseen target tasks compared to existing reinforcement learning and skill learning methods.

Examples of Learned Tasks and Skills

The proposed method is able to discover a diverse set of tasks and skills from the parameterized task space.

“Push dishware towards the bottom right”

“Push cans towards the upper left”

“Insert boxes into the rack on the left”

“Place dishware onto the rack on the right”

Inter-Skill Diversity

“Push boxes to upper left”

“Push boxes to the right”

“Push dishware objects to the upper left”

“Push dishware objects to the bottom left”

“Insert cans into the rack on the left”

“Place boxes onto the rack on the right”

“Insert dishware into the rack on the left”

“Place dishware onto the rack on the right”

Intra-Skill Diversity

“Insert boxes into the rack on the left”

Solving Unseen Target Tasks by Using Learned Skills

Given the learned skills, hierarchical reinforcement learning is applied to compose the skills for efficiently solving unseen tasks.