  Skill discovery methods enable agents to learn diverse emergent behaviors
without explicit rewards. To make learned skills useful for unknown downstream
tasks, obtaining a semantically diverse repertoire of skills is essential.
While some approaches introduce a discriminator to distinguish skills and
others aim to increase state coverage, no existing work directly addresses the
"semantic diversity" of skills. We hypothesize that leveraging the semantic
knowledge of large language models (LLMs) can lead us to improve semantic
diversity of resulting behaviors. In this sense, we introduce Language Guided
Skill Discovery (LGSD), a skill discovery framework that aims to directly
maximize the semantic diversity between skills. LGSD takes user prompts as
input and outputs a set of semantically distinctive skills. The prompts serve
as a means to constrain the search space into a semantically desired subspace,
and the generated LLM outputs guide the agent to visit semantically diverse
states within the subspace. We demonstrate that LGSD enables legged robots to
visit different user-intended areas on a plane by simply changing the prompt.
Furthermore, we show that language guidance aids in discovering more diverse
skills compared to five existing skill discovery methods in robot-arm
manipulation environments. Lastly, LGSD provides a simple way of utilizing
learned skills via natural language.
