Self-Supervised Anchoring of Fingertip Sensing to Proprioception and Proactive Actions for Robot Imitation Learning
This study addresses the challenge that fingertip sensing is susceptible to occlusion and difficult to integrate with vision-based policies, resulting in the underutilization of local interaction cues in imitation learning. To this end, this work proposes PROPRA, a framework that independently anchors fingertip pressure and proximity signals to proprioception and active actions for staged alignment. Furthermore, it introduces a novel proprioceptive-action self-supervised pretraining mechanism that effectively overcomes the difficulty of extracting sparse, phase-dependent sensor signals from limited demonstrations. Empirical evaluations on real-world manipulation tasks demonstrate that PROPRA significantly outperforms purely visual and image-anchored baselines, substantially improving success rates while preserving richer pre-contact state information.