[
    {
        "id": "osp-26769",
        "type": "article-journal",
        "title": "Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing",
        "author": [
            {
                "family": "Grimaldi",
                "given": "Matteo"
            },
            {
                "family": "Klee",
                "given": "David"
            },
            {
                "family": "Chen",
                "given": "Ziling"
            },
            {
                "family": "Jian",
                "given": "Tong"
            },
            {
                "family": "Lee",
                "given": "Wonju"
            },
            {
                "family": "Lu",
                "given": "Wenjie"
            },
            {
                "family": "Yu",
                "given": "Tao"
            },
            {
                "family": "Nabi",
                "given": "Saleh"
            }
        ],
        "URL": "https://omanscience.com/en/articles/touch2trace-tactile-driven-imitation-learning-for-dexterous-cable-tracing",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Dexterous manipulation of deformable objects demands continuous fingertip-level regulation of pressure, friction, and incipient slip. We study one of the most challenging cases: dexterous cable tracing, feeding a cable through the hand with repeated pinch-and-curl motions of the thumb and index finger. We introduce Touch2Trace, a tactile-driven imitation-learning system for this task, and provide, to our knowledge, the first systematic real-world characterization of how encoder pretraining, control rate, temporal context, and spatial resolution each shape policy performance. The winning learning recipe combines a tactile encoder pretrained for a custom 32 x 32 piezoresistive sensor (TacV5) via self-supervised learning with a lightweight transformer policy trained on teleoperated demonstrations via behavior cloning, deployed at 60 Hz on a Tesollo DG-5F hand. Tactile feedback without vision or explicit cable-state estimation significantly improves tracing performance versus a proprioception-only baseline: from 0.2 cm to 20.1 cm mean distance and 0% to 93% success rate, with zero-shot transfer to unseen cables and routing conditions. The results quantify the influence of key parameters in tactile-driven systems for reliable dexterous deformable object manipulation."
    }
]