ViTaMIn-B: 一种可靠且高效的视觉-触觉双臂操作接口
ViTaMIn-B: A Reliable and Efficient Visuo-Tactile Bimanual Manipulation Interface
AI总结：
ViTaMIn-B通过DuoTact传感器和6自由度双臂位姿采集技术，实现了高效可靠的双臂操作任务数据采集。
摘要：
手持设备已经为收集大规模、高质量的演示提供了前所未有的机会。然而，现有系统往往缺乏稳健的触觉传感或可靠的位姿跟踪，难以处理复杂的交互场景，尤其是双臂和接触密集的任务。在本工作中，我们提出了ViTaMIn-B，一种更强大且高效的用于此类任务的手持数据采集系统。我们首先设计了DuoTact，一种新型的视觉-触觉传感器，采用灵活的框架以承受操纵过程中的大接触力，同时捕捉高分辨率的接触几何。为了增强跨传感器的通用性，我们提出将传感器的全局变形重建为3D点云，并将其作为策略输入。我们进一步开发了一个稳健、统一的6自由度双臂位姿采集过程，使用Meta Quest控制器，这消除了传统SLAM方法中轨迹漂移的问题。全面的用户研究证实了ViTaMIn-B在新手和专家操作员中的高效性和高可用性。此外，对四个双臂操作任务的实验表明，其相对于现有系统的任务性能更优越。项目页面：https://chuanyune.github.io/ViTaMIn-B_page/
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
