{"ID":6497777,"CreatedAt":"2026-07-13T01:19:40.13847098Z","UpdatedAt":"2026-07-14T01:36:59.12045529Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.09190","arxiv_id":"2607.09190","title":"TactiDex: A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation","abstract":"Tactile feedback is fundamental to Hand-Object Interaction (HOI), governing contact formation, force regulation, and stable manipulation, making it essential for achieving true human-like dexterous manipulation. Yet, current human-to-robot dexterous transfer pipelines primarily rely on kinematic trajectories, resulting in motion imitation without physically grounded interaction. To address this, we introduce TactiDex, a real-world tactile-guided benchmark specifically designed to move dexterous manipulation beyond kinematic mimicry toward contact-level human-likeness. TactiDex provides a comprehensive dataset that elegantly aligns whole-hand tactile signals with multi-granularity kinematic and object states, coupled with standardized evaluation metrics. Building upon this data paradigm, we propose a tactile-driven transfer framework that effectively translates human demonstrations into physically plausible robotic execution. We introduce TactiSkill, a framework built upon a novel tri-component tactile reward that innovatively uses tactile signals as structured supervision. This reward unifies guidance, human-like alignment, and contact constraints into a single objective. Through comprehensive experiments on both single and bimanual tasks, we demonstrate that TactiSkill achieves superior performance in manipulation success and physical realism. This work lays a crucial foundation for advancing tactile-aware dexterous manipulation. Our project page at https://tactidex.github.io/.","short_abstract":"Tactile feedback is fundamental to Hand-Object Interaction (HOI), governing contact formation, force regulation, and stable manipulation, making it essential for achieving true human-like dexterous manipulation. Yet, current human-to-robot dexterous transfer pipelines primarily rely on kinematic trajectories, resulting...","url_abs":"https://arxiv.org/abs/2607.09190","url_pdf":"https://arxiv.org/pdf/2607.09190v1","authors":"[\"Suting Ni\",\"Hanbing Zhang\",\"Zhenyu Wei\",\"Guo Chen\",\"Chixuan Zhang\",\"Ye Shi\",\"Jingya Wang\"]","published":"2026-07-10T08:32:19Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}