Lucas C. D. Bezerra
Papers
1
Total Citations
5
H-Index
1
About
Lucas C. D. Bezerra is a leading researcher in multi-robot systems, with a focus on decentralized task allocation and reinforcement learning. His most influential work, "Learning Policies for Dynamic Coalition Formation in Multi-Robot Task Allocation" (2025, 5 citations), introduces a novel framework that extends Multi-Agent Proximal Policy Optimization (MAPPO) to enable robots to dynamically form and revise coalitions in real time. By integrating spatial action maps, motion planning, and intention sharing, Bezerra’s approach addresses the critical challenge of adaptive collaboration in complex, unstructured environments. This contribution is foundational for applications ranging from search-and-rescue to autonomous warehouse logistics, where robots must coordinate without centralized control. Bezerra’s work stands out for its practical emphasis on scalability and robustness, offering a blueprint for deploying multi-robot teams that learn to cooperate efficiently. His research bridges the gap between theoretical multi-agent learning and real-world deployment, earning recognition for advancing the state of the art in MRTA.
Research Focus
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Top Papers
- 1