让机器人理解用户意图，自动辅助完成复杂物品操作。
TASC: Task-Aware Shared Control for Relational Telemanipulation
- 通过视觉构建开放词汇交互图，从动作中推断任务意图。
- 实测效率提升，用户操作量减少，支持零样本泛化。
- 适合需要灵活协作的远程操控场景，如救援或制造。
我们提出TASC，一种面向关系型遥操作的任务感知共享控制框架，能够从仅含运动信息的输入中推断任务级用户意图并提供辅助。为支持无需预定义模板的抓取类关系任务，TASC基于视觉输入构建开放词汇的交互图来表示物体间的功能关系，并据此推断用户意图。共享控制策略在抓取和物体交互阶段提供辅助，由视觉语言模型预测的空间约束引导。该方法解决了共享控制下关系型遥操作的两大挑战：(1) 从低层运动指令中推断任务意图；(2) 在多样物体与任务间实现可泛化的辅助。仿真与真实世界实验表明，TASC相比先前方法显著提升了任务效率并减少了用户输入负担，同时实现了跨多样化关系型遥操作任务的零样本泛化。代码已公开于https://github.com/fitz0401/tasc。
原文摘要 · Abstract (English)
We present TASC, a Task-Aware Shared Control framework for relational telemanipulation that infers task-level user intent and provides assistance from motion-only input. To support prehensile relational tasks without predefined templates, TASC constructs an open-vocabulary interaction graph from visual input to represent functional object relationships, and infers user intent accordingly. A shared control policy then provides assistance during both grasping and object interaction, guided by spatial constraints predicted by a vision-language model. Our method addresses two key challenges in relational telemanipulation under shared control: (1) task-level intent inference from low-level motion commands, and (2) generalizable assistance across diverse objects and tasks. Experiments in both simulation and the real world demonstrate that TASC improves task efficiency and reduces user input effort compared to prior methods, while enabling zero-shot generalization across diverse relational telemanipulation tasks. The code that supports our experiments is publicly available at https://github.com/fitz0401/tasc.
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