TactiDex: A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation
TactiDex is a real-world tactile-guided benchmark designed to move dexterous manipulation beyond kinematic mimicry toward contact-level human-likeness. It provides a dataset aligning whole-hand tactile signals with multi-granularity kinematic and object states, and proposes TactiSkill, a framework using a tri-component tactile reward for transferring human demonstrations to robots. Experiments show superior performance in both single and bimanual tasks.
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[Submitted on 10 Jul 2026]
Title:TactiDex: A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation
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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 this https URL.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.09190 [cs.RO]
(or arXiv:2607.09190v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.09190
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Suting Ni [view email] [v1] Fri, 10 Jul 2026 08:32:19 UTC (10,475 KB)
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