DexTeleop-0: 面向共享自主的力感知双臂灵巧遥操作与自我中心感知
DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy
AI总结：
针对接触丰富任务中遥操作精度低的问题，提出基于触觉驱动的自适应策略，通过实时优化循环将粗略人类意图转化为精确力控机器人命令，显著提升任务成功率与执行效率。
摘要：
精细的双臂灵巧操作仍然是机器人学中的基础挑战。传统遥操作系统在接触丰富任务中常常失败，因为具身差距阻碍了精确的运动学映射，同时触觉和力反馈缺失。因此，高精度任务的数据收集效率仍然低得令人望而却步。为了解决这些限制，我们提出了一种触觉驱动的自适应策略，旨在在遥操作流水线之上实现精细操作。在我们的双臂灵巧框架DexTeleop-0中实例化，该策略引入了一个实时优化循环，通过将粗略的人类跟踪意图转化为精确的、力顺从的机器人命令并辅以触觉感知，弥合了具身差距。通过估计精确的接触点并利用触觉指尖力传感轮廓，系统使用相对于关节角度更新的操作空间雅可比动态计算局部修正。我们在模拟环境和真实硬件上严格评估了这种触觉驱动的自适应策略。与代表性基线相比，所提方法在鲁棒抓取、抗干扰操作和复杂灵巧任务中始终实现更高的任务成功率和改进的执行效率。
英文摘要：
Fine-grained, bimanual dexterous manipulation remains a foundational challenge in robotics. Traditional teleoperation systems often fail in contact-rich tasks because embodiment gaps hinder accurate kinematic mapping, while tactile and force feedback remain absent. Consequently, data collection efficiency for high-precision tasks remains prohibitively low. To address these limitations, we propose a tactile-driven adaptation strategy designed to enable fine-grained manipulation on top of teleoperation pipelines. Instantiated within our bimanual dexterous framework, DexTeleop-0, this strategy introduces a real-time optimization loop that bridges the embodiment gap by translating coarse human tracking intents into precise, force-compliant robotic commands with tactile sensing. By estimating accurate contact points and leveraging a tactile-enabled fingertip force-sensing profile, the system dynamically computes localized corrections using the operational space Jacobian with respect to joint angle updates. We rigorously evaluate this tactile-driven adaptation strategy across both simulated environments and real-world hardware. Compared with representative baselines, the proposed method consistently achieves higher task success rates and improved execution efficiency in robust grasping, disturbance-resilient manipulation, and complex dexterous tasks.
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。
