  Language-conditioned robotic skills make it possible to apply the high-level
reasoning of Large Language Models (LLMs) to low-level robotic control. A
remaining challenge is to acquire a diverse set of fundamental skills. Existing
approaches either manually decompose a complex task into atomic robotic actions
in a top-down fashion, or bootstrap as many combinations as possible in a
bottom-up fashion to cover a wider range of task possibilities. These
decompositions or combinations, however, require an initial skill library. For
example, a "grasping" capability can never emerge from a skill library
containing only diverse "pushing" skills. Existing skill discovery techniques
with reinforcement learning acquire skills by an exhaustive exploration but
often yield non-meaningful behaviors. In this study, we introduce a novel
framework for skill discovery that is entirely driven by LLMs. The framework
begins with an LLM generating task proposals based on the provided scene
description and the robot's configurations, aiming to incrementally acquire new
skills upon task completion. For each proposed task, a series of reinforcement
learning processes are initiated, utilizing reward and success determination
functions sampled by the LLM to develop the corresponding policy. The
reliability and trustworthiness of learned behaviors are further ensured by an
independent vision-language model. We show that starting with zero skill, the
ASD skill library emerges and expands to more and more meaningful and reliable
skills, enabling the robot to efficiently further propose and complete advanced
tasks. The project page can be found at:
https://agentic-skill-discovery.github.io.
