Language Guided Skill Discovery

The paper introduces Language Guided Skill Discovery (LGSD), a framework that utilizes Large Language Models (LLMs) to enhance the semantic diversity of skills learned by agents, enabling them to perform a variety of tasks more effectively and be controlled using natural language.

Abstract

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.

Method

The authors used a methodology that involves leveraging Large Language Models (LLMs) to guide the skill discovery process. They employed user prompts to constrain the search space to a semantically meaningful subspace, which helps in generating a set of semantically distinctive skills. The LLM outputs are used to guide the agent to visit semantically diverse states within the subspace. The skills are learned by maximizing the semantic diversity between them, and the framework allows for the utilization of these skills via natural language descriptions. The methodology is validated through experiments in both legged robot locomotion and robot-arm manipulation tasks, showing that LGSD outperforms existing skill discovery methods in terms of diversity and sample efficiency.

Main Finding

The authors discovered that by incorporating semantic guidance from Large Language Models (LLMs) into the skill discovery process, agents can learn a more diverse and semantically meaningful set of skills. They found that using user prompts to focus the search space on specific semantic aspects allows for the learning of skills that are not only diverse but also relevant to user-intended behaviors. Additionally, they demonstrated that the learned skills can be effectively utilized and controlled through natural language descriptions, providing a user-friendly interface for skill application. The experiments conducted in legged robot locomotion and robot-arm manipulation tasks showed that the Language Guided Skill Discovery (LGSD) framework outperforms existing methods in achieving semantic diversity and efficiency in skill learning.

Conclusion

The conclusion of the paper is that the Language Guided Skill Discovery (LGSD) framework successfully integrates semantic understanding from Large Language Models (LLMs) into the skill discovery process, leading to the learning of semantically diverse and meaningful skills by agents. The framework's ability to use natural language prompts to guide and constrain the search space results in skills that are not only varied but also aligned with human semantic expectations. The experiments conducted across different tasks confirm that LGSD is more efficient and effective in skill learning compared to existing methods, and it provides a natural language interface for utilizing the learned skills, marking a significant advancement in the field of skill discovery and reinforcement learning.

Keywords

Skill discovery, semantic diversity, large language models, natural language guidance, reinforcement learning, robot manipulation, legged robots, behavior learning, state space coverage, semantic distance, skill diversity, zero-shot learning, language models, skill discovery framework, behavior diversity, agent learning, downstream tasks, language guidance, skill utilization, natural language descriptions, semantic understanding, skill space constraint, language model outputs, skill discovery methods, robot tasks, skill-conditioned policy, language model guidance.