COMPASS：用空间 Transformer 实现大规模多机器人集群控制
Controlling Collectives of AI Agents in Reasoning Space with Spatial Transformers
多机器人协作的新玩法：COMPASS 让一群机器人自己本地算反馈，最多能带 1024 台一起飞，比集中式 LLM 策略稳不少。
研究提出 COMPASS，一种去中心化多机器人架构，通过空间 Transformer 在每台机器人本地聚合多跳消息生成反馈 token。实验对比集中式前沿 LLM 策略和纯语言通信消融，COMPASS 能形成一致的集群编队并准确执行指令。该系统零样本泛化到含义模糊的未见指令，可控制规模最高达训练 16 倍的集群，最多指挥 1024 台机器人。消融显示原始状态直接进入语言通道会破坏编队凝聚力。
Controlling Collectives of AI Agents in Reasoning Space with Spatial Transformers
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.
