手持式双臂操作数据采集系统，融合触觉与视觉，提升复杂任务可靠性。
ViTaMIn-B: A Reliable and Efficient Visuo-Tactile Bimanual Manipulation Interface
- 用柔性框架设计高分辨率触觉传感器，耐受大接触力并捕捉几何信息。
- 将传感器整体形变重建为3D点云，提升跨传感器泛化能力。
- 使用Meta Quest控制器实现无轨迹漂移的6自由度双臂位姿追踪，适合新手与专家使用。
手持设备为高效收集大规模高质量示范数据开辟了新可能。然而，现有系统在处理复杂交互场景时，常缺乏可靠的触觉感知或位姿追踪能力，尤其在双臂、高接触任务中表现不足。本文提出ViTaMIn-B，一种更强大且高效的此类任务数据采集系统。首先设计DuoTact，一种新型柔性框架触觉传感器，可承受操作中的大接触力，同时捕获高分辨率接触几何信息。为增强跨传感器泛化能力，提出将传感器全局形变重建为3D点云，并作为策略输入。进一步开发基于Meta Quest控制器的鲁棒统一6-DoF双臂位姿获取方法，有效消除常见SLAM方法中的轨迹漂移问题。用户研究证实其在新手与专家中均具高效性与高可用性。四个双臂操作任务实验表明，其任务表现优于现有系统。
原文摘要 · Abstract (English)
Handheld devices have opened up unprecedented opportunities to collect large-scale, high-quality demonstrations efficiently. However, existing systems often lack robust tactile sensing or reliable pose tracking to handle complex interaction scenarios, especially for bimanual and contact-rich tasks. In this work, we propose ViTaMIn-B, a more capable and efficient handheld data collection system for such tasks. We first design DuoTact, a novel compliant visuo-tactile sensor built with a flexible frame to withstand large contact forces during manipulation while capturing high-resolution contact geometry. To enhance the cross-sensor generalizability, we propose reconstructing the sensor's global deformation as a 3D point cloud and using it as the policy input. We further develop a robust, unified 6-DoF bimanual pose acquisition process using Meta Quest controllers, which eliminates the trajectory drift issue in common SLAM-based methods. Comprehensive user studies confirm the efficiency and high usability of ViTaMIn-B among novice and expert operators. Furthermore, experiments on four bimanual manipulation tasks demonstrate its superior task performance relative to existing systems. Project page: https://chuanyune.github.io/ViTaMIn-B_page/
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。