TacBootstrap: A Tactile-Informed Copilot for Contact-Rich Bimanual Manipulation from UMI Demonstrations

Contact-rich bimanual demonstration collection is difficult when visual teleoperation cannot resolve local contact. We present TacBootstrap, a tactile-informed, phase-gated copilot trained exclusively on Universal Manipulation Interface (UMI) demonstrations. Both UMI and robot grippers use fingertip Daimon vision-based tactile sensors; their shear and depth channels provide the policy’s tactile inputs. The operator controls non-contact motion through virtual reality (VR), then manually transfers authority near contact while VR actions pause. A Slow-Fast encoder combines wrist-image context with three frames of tactile and robot-state observations. Current-state retrieval provides a demonstrated trajectory anchor, and an anchor-based flow head generates bimanual local-pose and gripper action chunks for closed-loop execution. Robot recordings are used only for evaluation. On paper-cup separation, cap tightening, and network-cable insertion, TacBootstrap achieves 77.7% task-macro success versus 55.4% for robot VR, with higher success in every task; direct UMI achieves 100%. Task-level attempts per success decrease from 1.81 to 1.29. Recorded durations are also lower, although differing recording boundaries limit efficiency comparisons. These system-level results support leveraging portable visual-tactile demonstrations for robot-side contact assistance.