← 研踪 PaperTrailarXiv:2606.23431 · v2
DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy
首次提交 2026-06-22 22:48
版本更新 2026-11-06 01:16
雷达观测 2026-11-09 10:16
摘要
Fine-grained, bimanual dexterous manipulation remains a foundational challenge in robotics. Embodiment gaps hinder accurate kinematic mapping, and visual tracking alone does not regulate contact forces. 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 adds robot-side contact corrections to human tracking commands using fingertip feedback. By estimating contact positions and forces, the system dynamically computes localized corrections using a contact-stiffness approximation and the kinematic Jacobian, with per-contact vector force tracking, soft predicted-contact-moment regularization, and bounded hand-joint residuals. We evaluate this tactile-driven adaptation strategy across both simulated environments and real-world hardware. Controlled simulation replay yields final-stage success rates of 91% for Ball Assembly and 62% for Stir in Cup, versus 88% and 49% for force tracking alone. Fixed-input replay on two hand types, seven-operator teleoperation, and external-force traces further assess contact assistance. The open-sourced project page can be found at https://henryhcliu.github.io/dexteleop-0.
作者备注：16 pages, 9 figures, 6 tables
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v2 · 2026-11-06 01:16 · 最新观测版本
DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy
Fine-grained, bimanual dexterous manipulation remains a foundational challenge in robotics. Embodiment gaps hinder accurate kinematic mapping, and visual tracking alone does not regulate contact forces. 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 adds robot-side contact corrections to human tracking commands using fingertip feedback. By estimating contact positions and forces, the system dynamically computes localized corrections using a contact-stiffness approximation and the kinematic Jacobian, with per-contact vector force tracking, soft predicted-contact-moment regularization, and bounded hand-joint residuals. We evaluate this tactile-driven adaptation strategy across both simulated environments and real-world hardware. Controlled simulation replay yields final-stage success rates of 91% for Ball Assembly and 62% for Stir in Cup, versus 88% and 49% for force tracking alone. Fixed-input replay on two hand types, seven-operator teleoperation, and external-force traces further assess contact assistance. The open-sourced project page can be found at https://henryhcliu.github.io/dexteleop-0.
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