6 papers
Threading Optimization for Vision-Language-Action Model Inference in Low-Cost Smart Agricultural Manipulation
Keith Truongcao, Christopher Nhu, Zijian An +3
Vision-Language Action (VLA) models continue to face challenges such as slow inference speed and difficulty performing fine-grained motion adjustments, limiting their widespread ad…
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…
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…
Policy-Guided World Model Planning for Language-Conditioned Visual Navigation
Amirhosein Chahe, Lifeng Zhou
Navigating to a visually specified goal given natural language instructions remains a fundamental challenge in embodied AI. Existing approaches either rely on reactive policies tha…
LLM-Flock: Decentralized Multi-Robot Flocking via Large Language Models and Influence-Based Consensus
Peihan Li, Lifeng Zhou
Large Language Models (LLMs) have advanced rapidly in recent years, demonstrating strong capabilities in problem comprehension and reasoning. Inspired by these developments, resear…
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…