DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy

Abstract

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.