arXiv:2606.23431v2 Announce Type: replace Abstract: 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.