1 citations · 4 across the 9 of their papers we have counts for
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Hierarchical LLMs In-the-Loop Optimization for Real-Time Multi-Robot Target Tracking under Unknown Hazards
Yuwei Wu, Yuezhan Tao, Peihan Li +4
Real-time multi-robot coordination in hazardous and adversarial environments requires fast, reliable adaptation to dynamic threats. While Large Language Models (LLMs) offer strong…
Resilient Multi-Robot Target Tracking with Sensing and Communication Danger Zones
Peihan Li, Yuwei Wu, Jiazhen Liu +3
Multi-robot collaboration for target tracking in adversarial environments poses significant challenges, including system failures, dynamic priority shifts, and other unpredictable…
Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation
Seungyeop Lee, Knut Peterson, Solmaz Arezoomandan +4
A major obstacle to the development of effective monocular depth estimation algorithms is the difficulty in obtaining high-quality depth data that corresponds to collected RGB imag…
Multi-Robot Target Tracking with Sensing and Communication Danger Zones
Jiazhen Liu, Peihan Li, Yuwei Wu +3
Multi-robot target tracking finds extensive applications in different scenarios, such as environmental surveillance and wildfire management, which require the robustness of the pra…
Challenges Faced by Large Language Models in Solving Multi-Agent Flocking
Peihan Li, Vishnu Menon, Bhavanaraj Gudiguntla +2
Flocking is a behavior where multiple agents in a system attempt to stay close to each other while avoiding collision and maintaining a desired formation. This is observed in the n…