Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?
arxiv(2023)
摘要
A flurry of recent work has demonstrated that pre-trained large language
models (LLMs) can be effective task planners for a variety of single-robot
tasks. The planning performance of LLMs is significantly improved via prompting
techniques, such as in-context learning or re-prompting with state feedback,
placing new importance on the token budget for the context window. An
under-explored but natural next direction is to investigate LLMs as multi-robot
task planners. However, long-horizon, heterogeneous multi-robot planning
introduces new challenges of coordination while also pushing up against the
limits of context window length. It is therefore critical to find
token-efficient LLM planning frameworks that are also able to reason about the
complexities of multi-robot coordination. In this work, we compare the task
success rate and token efficiency of four multi-agent communication frameworks
(centralized, decentralized, and two hybrid) as applied to four
coordination-dependent multi-agent 2D task scenarios for increasing numbers of
agents. We find that a hybrid framework achieves better task success rates
across all four tasks and scales better to more agents. We further demonstrate
the hybrid frameworks in 3D simulations where the vision-to-text problem and
dynamical errors are considered. See our project website
https://yongchao98.github.io/MIT-REALM-Multi-Robot/ for prompts, videos, and
code.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要