Paper ID: 2412.20397 • Published Dec 29, 2024
Learning Policies for Dynamic Coalition Formation in Multi-Robot Task Allocation
Lucas C. D. Bezerra, Ataíde M. G. dos Santos, Shinkyu Park
TL;DR
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We propose a decentralized, learning-based framework for dynamic coalition
formation in Multi-Robot Task Allocation (MRTA). Our approach extends
Multi-Agent Proximal Policy Optimization (MAPPO) by integrating spatial action
maps, robot motion planning, intention sharing, and task allocation revision to
enable effective and adaptive coalition formation. Extensive simulation studies
confirm the effectiveness of our model, enabling each robot to rely solely on
local information to learn timely revisions of task selections and form
coalitions with other robots to complete collaborative tasks. Additionally, our
model significantly outperforms existing methods, including a market-based
baseline. Furthermore, we evaluate the scalability and generalizability of the
proposed framework, highlighting its ability to handle large robot populations
and adapt to scenarios featuring diverse task sets.