AssistantX: LLMを活用した人間共存環境における能動的アシスタント
AssistantX: An LLM-Powered Proactive Assistant in Collaborative Human-Populated Environment
4つのLLMエージェントからなるマルチエージェント構成で、人間がいる実環境で自律的に動作し、指示への反応や状況に応じた戦略調整、人間への能動的な支援要請を行うアシスタントを提案した。
著者: Nan Sun, Bo Mao, Yongchang Li, Di Guo, Huaping Liu
分類: cs.RO, cs.AI, cs.MA
原文アブストラクト
Current service robots suffer from limited natural language communication abilities, heavy reliance on predefined commands, ongoing human intervention, and, most notably, a lack of proactive collaboration awareness in human-populated environments. This results in narrow applicability and low utility. In this paper, we introduce AssistantX, an LLM-powered proactive assistant designed for autonomous operation in realworld scenarios with high accuracy. AssistantX employs a multi-agent framework consisting of 4 specialized LLM agents, each dedicated to perception, planning, decision-making, and reflective review, facilitating advanced inference capabilities and comprehensive collaboration awareness, much like a human assistant by your side. We built a dataset of 210 real-world tasks to validate AssistantX, which includes instruction content and status information on whether relevant personnel are available. Extensive experiments were conducted in both text-based simulations and a real office environment over the course of a month and a half. Our experiments demonstrate the effectiveness of the proposed framework, showing that AssistantX can reactively respond to user instructions, actively adjust strategies to adapt to contingencies, and proactively seek assistance from humans to ensure successful task completion. More details and videos can be found at https://assistantx-agent.github.io/AssistantX/.
