T-FRAME: Tactile Force-aware Representation for Action-conditioned Manipulability Evolution

Reliable dexterous fastening requires repeated contact renewal until a part reaches its seat. Maximizing the current turn can compromise release and recontact, making immediate progress a poor guide to sustained operation. We introduce T-FRAME, which learns action-conditioned contact events, loads, and remaining manipulation resources alongside continuation value. Physical outcomes supervise action consequences, while preferences train choices among alternatives from the same contact state. Uncertain contact and relative-pressure evidence from human video shapes these preferences; tactile-conditioned GPU search generates robot-native trajectories evaluated through physical rollouts. Selected trajectories and task reinforcement learning train a recurrent policy that adjusts finger and wrist commands using live touch, without online search. A finite-candidate analysis separates action coverage, teacher alignment, and selection error. Across simulated fastening tasks with different head shapes, diameters, and stable or initially loose engagement, T-FRAME achieves near-complete insertion progress and shorter successful completion times than the adapted baselines. Ablations link human evidence to execution efficiency, directional touch to reduced backdrive, and current feedback to sustained completion. These results support learning contact consequences as a basis for reliable, efficient long-horizon fastening.