Conversational Language Models for Human-in-the-Loop Multi-Robot Coordination
CoRR(2024)
摘要
With the increasing prevalence and diversity of robots interacting in the
real world, there is need for flexible, on-the-fly planning and cooperation.
Large Language Models are starting to be explored in a multimodal setup for
communication, coordination, and planning in robotics. Existing approaches
generally use a single agent building a plan, or have multiple homogeneous
agents coordinating for a simple task. We present a decentralised, dialogical
approach in which a team of agents with different abilities plans solutions
through peer-to-peer and human-robot discussion. We suggest that argument-style
dialogues are an effective way to facilitate adaptive use of each agent's
abilities within a cooperative team. Two robots discuss how to solve a cleaning
problem set by a human, define roles, and agree on paths they each take. Each
step can be interrupted by a human advisor and agents check their plans with
the human. Agents then execute this plan in the real world, collecting rubbish
from people in each room. Our implementation uses text at every step,
maintaining transparency and effective human-multi-robot interaction.
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