Agentic Skill Discovery（智能体技能发现）
Agentic Skill Discovery
AI总结：
针对机器人基础技能获取难题，提出完全由大语言模型驱动的技能发现框架，通过LLM生成任务提案、采样奖励与判定函数结合强化学习训练策略，经视觉语言模型保障可靠性，实现从零构建可扩展的有意义技能库。
摘要：
语言条件化机器人技能使得将大语言模型（LLMs）的高层推理能力应用于低层机器人控制成为可能。目前仍存在的一个挑战是获取多样化的基础技能集合。现有方法要么以自上而下的方式将复杂任务手动分解为原子级机器人动作，要么以自下而上的方式生成尽可能多的组合以覆盖更广泛的任务可能性。但这类分解或组合都需要初始的技能库。例如，“抓取”能力永远无法从仅包含各类“推动”技能的技能库中产生。现有的基于强化学习的技能发现技术通过穷尽式探索获取技能，但往往会产生无意义的行为。本研究提出了一种完全由LLM驱动的新型技能发现框架。该框架首先由LLM根据提供的场景描述和机器人配置生成任务提案，目标是在完成任务后逐步获取新技能。对于每个提出的任务，会启动一系列强化学习流程，利用LLM采样的奖励函数和成功判定函数来训练对应的策略。独立的视觉-语言模型会进一步确保所学行为的可靠性和可信度。我们的研究表明，从零技能起步，技能库会逐步形成并扩展出越来越多有意义且可靠的技能，使机器人能够高效地进一步提出并完成更高级的任务。项目页面：https://agentic-skill-discovery.github.io
英文摘要：
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 skill library emerges and expands to more and more meaningful and reliable skills, enabling the robot to efficiently further propose and complete advanced tasks. Project page: \url{https://agentic-skill-discovery.github.io}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
