AI 中文总结
针对开放式动态目标，提出基于粒子滤波的目标导向共享自主方法，利用基础模型生成语义目标与辅助，用户研究表明可减少遥操作时间并提升满意度。
AI 中文摘要
共享自主的一种常见方法是将人类输入与基于人类可能目标的自主辅助相结合。然而，大多数现有方法假设一组静态的可能目标事先已知，这限制了此类方法在非结构化辅助环境中的使用。我们转而研究如何实现具有开放式和动态变化目标的共享自主。我们将目标导向的共享自主表述为粒子滤波器，其中粒子代表候选的人类目标。与具有固定目标集的传统方法不同，我们的转移模型随着交互的进行动态提出新的候选目标，人类行为实时更新对这些目标的信念。我们用基础模型（例如，视觉基础模型和大型语言模型）实例化该框架，这些模型提出上下文相关的语义目标，从低级技能原语生成目标条件辅助，并根据人类修正改进这些技能。我们通过一项用户研究评估了我们的方法，其中12名参与者使用我们的方法和最先进的共享自主基线执行各种桌面操作任务。结果表明，基于粒子滤波器的方法减少了用户远程操作系统的时间，并提高了用户满意度。用户研究视频：此 https URL
英文摘要
A common approach for shared autonomy blends human inputs with autonomous assistance based on the human's likely goal. However, most existing approaches assume that a static set of possible goals is known a priori, which limits the use of such methods in unstructured assistive settings. We instead investigate how to enable shared autonomy with open-ended and dynamically changing goals. We formulate goal-oriented shared autonomy as a particle filter in which particles represent candidate human goals. Unlike conventional approaches with a fixed goal set, our transition model dynamically proposes new candidate goals as the interaction evolves, and human actions update the belief over these goals in real time. We instantiate this framework with foundation models (e.g., vision grounding and large language models) that propose context-relevant semantic goals, generate goal-conditioned assistance from low-level skill primitives, and refine those skills from human corrections. We assess our approach through a user study where 12 participants perform a variety of tabletop manipulation tasks with our method and state-of-the-art shared autonomy baselines. The results show that our particle filter-based approach reduces the amount of time users spend teleoperating the system and improves user satisfaction. User study videos: https://youtu.be/Ii26XuRqm9c
