An agent assisting humans in daily living activities can collaborate more effectively by anticipating upcoming tasks. Data-driven methods represent the state of the art in task anticipation, planning, and related problems, but these methods are resource-hungry and opaque. Our prior work introduced a proof of concept framework that used an LLM to anticipate 3 high-level tasks that served as goals for a classical planning system that computed a sequence of low-level actions for the agent to achieve these goals. This paper describes DaTAPlan, our framework that significantly extends our prior work toward human-robot collaboration. Specifically, DaTAPlan planner computes actions for an agent and a human to collaboratively and jointly achieve the tasks anticipated by the LLM, and the agent automatically adapts to unexpected changes in human action outcomes and preferences. We evaluate DaTAPlan capabilities in a realistic simulation environment, demonstrating accurate task anticipation, effective human-robot collaboration, and the ability to adapt to unexpected changes. Project website: https://dataplan-hrc.github.io

一个名为DaTAPlan的框架扩展了之前的工作，使用低层次学习模型（LLM）来预测高级任务，使得一个代理人和人类能够协同合作通过经典规划系统计算出的低级行动共同实现这些任务，还能自适应地适应人类行为结果和偏好的意外变化，通过在真实的仿真环境中进行评估展示了准确的任务预测、有效的人机协作以及适应意外变化的能力。

数据驱动的任务预测与知识驱动的人机协作规划