基于空间Transformer的推理空间AI智能体群体控制

中文摘要 AI 辅助

大型语言模型（LLMs）为机器人规划与导航引入了令人兴奋的新范式，但随着团队规模的增大，即使在简单的多机器人任务中也会失败。我们提出COMPASS，一种可扩展的、去中心化的多机器人架构，用于通过推理空间反馈控制来控制大型智能体机器人群体。反馈由每台机器人上的空间Transformer局部生成，该Transformer将整个机群中的多跳消息聚合为学习得到的反馈令牌。我们的实验发现，语言模型群体在输入命令的结构化多样性下表现出性能提升，这种多样性可以抵消偏差；这一优势在规模上得以保持。与集中式前沿LLM策略和仅语言通信的消融实验相比，我们发现COMPASS的耦合设计决定性地产生了内聚的编队飞行，准确执行所命令的意图。我们表明，推理反馈在与紧凑的学习令牌组合时效果最佳。我们的消融实验表明，手工设计的反馈若将原始状态直接放入语言通道，会破坏内聚性。COMPASS能够零样本泛化到含义模糊的未见指令，同时指挥规模达其训练规模16倍的机群，在自然语言命令下飞行多达1024个机器人。

英文摘要

Large Language Models (LLMs) introduce an exciting new paradigm for planning and navigation in robotics, but fail on even simple multi-robot tasks as team sizes grow. We propose COMPASS, a scalable, decentralized multi-robot architecture for controlling large collectives of agentic robots with reasoning space feedback control. Feedback is generated locally on each robot by a spatial transformer which aggregates multi-hop messages across the fleet into a learned feedback token. Our experiments find that collectives of language models demonstrate performance gains from structured diversity of the input command, which can cancel biases; an advantage that is held across scale. Compared against a centralized frontier LLM policy and a language-only communication ablation, we find that the coupled design of COMPASS decisively produces cohesive flocking formations that accurately fly the commanded intent. We show that reasoning feedback works best when composed with a compact learned token. Our ablations show that hand engineered feedback with raw state appearing in the language channel obliterates cohesion. COMPASS generalizes zero-shot to unseen instructions of ambiguous meaning while commanding flocks up to 16 times its training scale, flying up to 1024 robots under natural language commands.

发表机构

• University of Pennsylvania（宾夕法尼亚大学）

机构由 AI 辅助整理，请以论文原文为准。