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
GG-LLM: Geometrically Grounding Large Language Models for Zero-shot Human Activity Forecasting in Human-Aware Task Planning
Moritz A. Graule, Volkan Isler
Scalable underwater assembly with reconfigurable visual fiducials
Samuel Lensgraf, Ankita Sarkar, Adithya Pediredla, Devin Balkcom, Alberto Quattrini Li
Online Learning and Planning in Cognitive Hierarchies
Bernhard Hengst, Maurice Pagnucco, David Rajaratnam, Claude Sammut, Michael Thielscher
Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking
Yongqi Tong, Yifan Wang, Dawei Li, Sizhe Wang, Zi Lin, Simeng Han, Jingbo Shang
Interactive Task Planning with Language Models
Boyi Li, Philipp Wu, Pieter Abbeel, Jitendra Malik
Adaptive Robot Assistance: Expertise and Influence in Multi-User Task Planning
Abhinav Dahiya, Stephen L. Smith
BEVGPT: Generative Pre-trained Large Model for Autonomous Driving Prediction, Decision-Making, and Planning
Pengqin Wang, Meixin Zhu, Hongliang Lu, Hui Zhong, Xianda Chen, Shaojie Shen, Xuesong Wang, Yinhai Wang
Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning
Dhruv Shah, Michael Equi, Blazej Osinski, Fei Xia, Brian Ichter, Sergey Levine