Authors: Xiaopan Zhang, Hao Qin, Fuquan Wang, Yue Dong, Jiachen Li
Abstract: Language models (LMs) possess a strong capability to comprehend natural
language, making them effective in translating human instructions into detailed
plans for simple robot tasks. Nevertheless, it remains a significant challenge
to handle long-horizon tasks, especially in subtask identification and
allocation for cooperative heterogeneous robot teams. To address this issue, we
propose a Language Model-Driven Multi-Agent PDDL Planner (LaMMA-P), a novel
multi-agent task planning framework that achieves state-of-the-art performance
on long-horizon tasks. LaMMA-P integrates the strengths of the LMs’ reasoning
capability and the traditional heuristic search planner to achieve a high
success rate and efficiency while demonstrating strong generalization across
tasks. Additionally, we create MAT-THOR, a comprehensive benchmark that
features household tasks with two different levels of complexity based on the
AI2-THOR environment. The experimental results demonstrate that LaMMA-P
achieves a 105% higher success rate and 36% higher efficiency than existing
LM-based multi-agent planners. The experimental videos, code, and datasets of
this work as well as the detailed prompts used in each module are available at
https://lamma-p.github.io.
Source: http://arxiv.org/abs/2409.20560v1
