统一框架实现机器人任务中人机协作的自适应力反馈切换。
A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures
- 基于概率动力系统实现粗略引导的自主控制
- 支持手动、半自动到全自主模式无缝切换，降低交互力
- 适用于精密操作场景，适合需要高精度协作的研究者
概率虚拟固定装置（VFs）可根据学习或感知的不确定性，自适应选择各任务阶段最合适的触觉反馈。尽管保持人在回路中对保证高精度至关重要，但部分任务阶段的适度自动化对于提升效率同样关键。本文提出一种统一的概率虚拟固定框架，可无缝切换手动、半自动（人类负责精密操作）和全自主模式。引入一种新型基于概率动力系统的虚拟固定装置，用于粗略引导，使机器人在保持人在回路的前提下自主完成部分任务阶段。针对需高精度引导的任务，将概率位置基轨迹固定装置扩展为支持自动化，实现无缝人机交互、几何感知及最优阻抗增益。对于极需精确引导的手动任务，还将视觉伺服固定装置扩展为具备相同几何感知与阻抗行为。我们在多种机器人上验证该方法，包括专家用户评估，结果表明其操作模式灵活、编程简便，相比基线方案交互力更低，用户体验更优。
原文摘要 · Abstract (English)
Probabilistic Virtual Fixtures (VFs) enable the adaptive selection of the most suitable haptic feedback for each phase of a task, based on learned or perceived uncertainty. While keeping the human in the loop remains essential, for instance, to ensure high precision, partial automation of certain task phases is critical for productivity. We present a unified framework for probabilistic VFs that seamlessly switches between manual fixtures, semi-automated fixtures (with the human handling precise tasks), and full autonomy. We introduce a novel probabilistic Dynamical System-based VF for coarse guidance, enabling the robot to autonomously complete certain task phases while keeping the human operator in the loop. For tasks requiring precise guidance, we extend probabilistic position-based trajectory fixtures with automation, allowing for seamless human interaction, geometry-awareness and optimal impedance gains. For manual tasks requiring very precise guidance, we also extend visual servoing fixtures with the same geometry-awareness and impedance behavior. We validate our approach on different robots, including an evaluation with expert users, showcasing operation modes, the ease of programming fixtures and lower interaction forces and favorable usability compared to a baseline.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。