发表机构
Shenzhen Technology University; Sino-German College of Intelligent Manufacturing, Shenzhen Technology University; LS Wiiri Robot Innovation Center（深圳技术大学; 深圳技术大学中德智能制造学院; LS Wiiri机器人创新中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出M3-Tele多模态遥操作框架，集成全身协调与柔顺控制，显著降低力跟踪误差和接触丢失，提升接触密集移动操作性能。
AI 中文摘要
高效执行接触密集任务需要全身协调与物理柔顺调节的无缝集成。然而，现有的遥操作和数据采集框架往往忽视了多模态感知与协调全身操作的联合考虑。这一局限性会降低示范采集的效率和质量，从而影响下游策略学习的效果。在本工作中，我们提出了M3-Tele：一个面向柔顺全身移动操作的多模态遥操作统一框架，该框架能够在任务执行过程中实现稳定的物理交互，并捕获对齐的视觉、触觉、力觉和本体感觉观测数据。大量实验表明，所提出的框架显著提升了接触密集遥操作的性能。所提出的控制器将力跟踪误差从4.132牛降至0.346牛，接触丢失从每次试验2.46次降至0.02次，触觉形变误差降低了65%。跨四个移动操作任务的用户研究也验证了所提出系统的可靠性和可用性。此外，扩散策略实验凸显了联合触觉和力觉感知的价值。
英文摘要
Executing contact-rich tasks efficiently requires the seamless integration of whole-body coordination and physical compliance regulation. However, existing teleoperation and data-collection frameworks often overlook the joint consideration of multimodal perception and coordinated whole-body operation. This limitation can reduce the efficiency and quality of demonstration collection, thereby affecting the effectiveness of downstream policy learning. In this work, we present \textbf{M3-Tele}: A Unified \underline{M}ultimodal \underline{Tele}operational Framework for Compliant Whole-Body \underline{M}obile \underline{M}anipulation, enabling stable physical interaction and capturing aligned visual, tactile, force, and proprioceptive observations during task execution. Extensive experiments demonstrate that the proposed framework significantly improves contact-rich teleoperation performance. The proposed controller reduces the force tracking error from 4.132~N to 0.346~N, the contact loss from 2.46 to 0.02 events per trial and the tactile deformation error by 65\%. User studies across four mobile manipulation tasks also verify the reliability and usability of the proposed system. Furthermore, Diffusion Policy experiments highlight the value of joint tactile and force sensing.
Comments15 pages,12 figures,a under-review journal
