1 citations · 4 across the 9 of their papers we have counts for
10 papers
LLM-Foraging: Large Language Models for Decentralized Swarm Robot Foraging
Peihan Li, Joanna Gutierrez, Fabian Hernandez +2
Swarm foraging algorithms, such as the central-place foraging algorithm (CPFA), typically rely on offline parameter optimization using genetic algorithms (GA) or reinforcement lear…
Failure-Aware Multi-Robot Coordination for Resilient and Adaptive Target Tracking
Peihan Li, Jiazhen Liu, Yuwei Wu +1
Multi-robot coordination is crucial for autonomous systems, yet real-world deployments often encounter various failures. These include both temporary and permanent disruptions in s…
LLM-Flock: LLM-Driven Decentralized Multi-Robot Flocking via Influence-Based Consensus with Collision Avoidance
Peihan Li, Lifeng Zhou
Large Language Models (LLMs) have shown strong capabilities in reasoning and planning, prompting researchers to explore their use as decentralized decision-makers for multi-robot f…
Large Language Models for Multi-Robot Systems: A Survey
Peihan Li, Zijian An, Shams Abrar +1
The rapid advancement of Large Language Models (LLMs) has opened new possibilities in Multi-Robot Systems (MRS), enabling enhanced communication, task allocation and planning, and…
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…
Learning Decentralized Flocking Controllers with Spatio-Temporal Graph Neural Network
Siji Chen, Yanshen Sun, Peihan Li +2
Recently a line of researches has delved the use of graph neural networks (GNNs) for decentralized control in swarm robotics. However, it has been observed that relying solely on t…