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
Embodied Task Planning with Large Language Models
Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan
Task Planning Support for Arborists and Foresters: Comparing Deep Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on UAV-Data
Jonas-Dario Troles, Richard Nieding, Sonia Simons, Ute Schmid