DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy
2026-06-22 • Robotics
Robotics
AI summaryⓘ
The authors address the challenge of controlling robot hands to do precise, two-handed tasks. They note that current systems struggle because the robot doesn't perfectly mimic human hand movements, and there is no touch or force feedback for adjustment. To improve this, the authors developed a method that uses touch sensors on the robot's fingertips to make real-time corrections, helping the robot better follow the human's intentions. Their system, tested in simulations and real robots, showed better success and efficiency in delicate and complex manipulation tasks than other approaches.
bimanual manipulationteleoperationtactile sensingforce feedbackkinematic mappingoperational space Jacobiandexterous roboticsreal-time optimizationcontact-rich tasks
Authors
Haichao Liu, Yuyao Jiang, Hyunsun Park, Yuanjiang Xue, Ziwei Wang
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.