Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal Constraints

no code implementations • 13 Feb 2024 • Yu Quan Chong, Jiaoyang Li, Katia Sycara

To incorporate task assignment, path planning, and a user-defined objective into a coherent framework, this paper examines the Task Assignment and Path Finding with Precedence and Temporal Constraints (TAPF-PTC) problem.

Multi-Agent Path Finding Reinforcement Learning (RL)

Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis

no code implementations • 14 Dec 2023 • Yafei Hu, Quanting Xie, Vidhi Jain, Jonathan Francis, Jay Patrikar, Nikhil Keetha, Seungchan Kim, Yaqi Xie, Tianyi Zhang, Shibo Zhao, Yu Quan Chong, Chen Wang, Katia Sycara, Matthew Johnson-Roberson, Dhruv Batra, Xiaolong Wang, Sebastian Scherer, Zsolt Kira, Fei Xia, Yonatan Bisk

Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models (i. e., foundation models) in research fields such as Natural Language Processing (NLP) and Computer Vision (CV), we devote this survey to exploring (i) how these existing foundation models from NLP and CV can be applied to the field of robotics, and also exploring (ii) what a robotics-specific foundation model would look like.

Theory of Mind for Multi-Agent Collaboration via Large Language Models

no code implementations • 16 Oct 2023 • Huao Li, Yu Quan Chong, Simon Stepputtis, Joseph Campbell, Dana Hughes, Michael Lewis, Katia Sycara

While Large Language Models (LLMs) have demonstrated impressive accomplishments in both reasoning and planning, their abilities in multi-agent collaborations remains largely unexplored.

Hallucination Multi-agent Reinforcement Learning