通过触觉反馈实现双手灵巧操作的实时力控优化。
DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy
- 基于触觉感知实时优化人类意图，弥补操作者与机器人之间的身体差异。
- 在仿真和真实硬件上均显著提升抓取成功率与抗干扰能力。
- 适合需要高精度灵巧操作的工业装配与远程操控场景。
精细的双手灵巧操作仍是机器人领域的基础挑战。传统遥操作系统的触觉与力反馈缺失，且因本体差异导致运动映射不准，致使高精度任务的数据采集效率极低。为此，我们提出一种触觉驱动的自适应策略，集成于双臂灵巧操作框架DexTeleop-0中。该策略通过实时优化循环，利用触觉感知估计准确接触点，并结合指尖力感测模型，基于操作空间雅可比矩阵动态计算关节角度修正量，实现力顺应的精准控制。我们在仿真环境与真实硬件上对方法进行了严格评估。相比代表性基线，该方法在稳健抓取、抗扰动操作及复杂灵巧任务中均展现出更高的任务成功率与执行效率。
原文摘要 · Abstract (English)
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.
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