Attention Is the Message: Communicating through Planner Attention for Multi-Robot Embodied Planning

Robots that share a semantic map still spend computation encoding the same regions in their onboard planners. Can they also share the planner states computed from those regions, even as the map changes and each robot pursues a different query? We introduce region-addressed attention messages, which let robots with frozen planners reuse one another’s computation without knowing the receiver’s query or training for communication. Each room has an owner that encodes it using only a shared prefix and that room’s evidence. The resulting attention state is tagged with the room identity, evidence version, and owner. Receivers accept a message only when these tags match the current map, then use it directly through the planner’s native attention. This construction ensures that every accepted message equals the state the receiver would compute itself. With two robots, attention messages reduce planner GPU time by 45% compared with cached re-perception. Across teams of two to four robots, they match the computational cost of a centralized shared cache while keeping planning onboard. Experiments on HM3D and MP3D show that sharing room information improves navigation success, with re-perception recovering most of the same gains. Our contribution is to achieve these benefits at lower computational cost. We further validate the system through real-world navigation experiments with two physical robots, demonstrating shared planner computation with onboard planning.