Task Planning
Task planning in artificial intelligence focuses on enabling agents, both virtual and robotic, to autonomously generate sequences of actions to achieve specified goals. Current research emphasizes improving the robustness and efficiency of planning methods, particularly using large language models (LLMs) and visual language models (VLMs), often integrated with symbolic planning techniques or reinforcement learning, to handle complex, long-horizon tasks and multi-agent scenarios. This field is crucial for advancing embodied AI, improving decision-making in various domains (e.g., disaster response, robotics, game design), and developing more reliable and adaptable autonomous systems.
Papers
Planning, Living and Judging: A Multi-agent LLM-based Framework for Cyclical Urban Planning
Hang Ni, Yuzhi Wang, Hao Liu
A Predefined-Time Convergent and Noise-Tolerant Zeroing Neural Network Model for Time Variant Quadratic Programming With Application to Robot Motion Planning
Yi Yang, Xuchen Wang, Richard M. Voyles, Xin Ma
Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following
Yuxiao Yang, Shenao Zhang, Zhihan Liu, Huaxiu Yao, Zhaoran Wang
Scalable Hierarchical Reinforcement Learning for Hyper Scale Multi-Robot Task Planning
Xuan Zhou, Xiang Shi, Lele Zhang, Chen Chen, Hongbo Li, Lin Ma, Fang Deng, Jie Chen
ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning
Yi Huang, Fangyin Cheng, Fan Zhou, Jiahui Li, Jian Gong, Hongjun Yang, Zhidong Fan, Caigao Jiang, Siqiao Xue, Faqiang Chen
Planning Human-Robot Co-manipulation with Human Motor Control Objectives and Multi-component Reaching Strategies
Kevin Haninger, Luka Peternel
Equivariant Action Sampling for Reinforcement Learning and Planning
Linfeng Zhao, Owen Howell, Xupeng Zhu, Jung Yeon Park, Zhewen Zhang, Robin Walters, Lawson L.S. Wong
Loosely Synchronized Rule-Based Planning for Multi-Agent Path Finding with Asynchronous Actions
Shuai Zhou, Shizhe Zhao, Zhongqiang Ren