TactiDex: A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation

• URL: http://arxiv.org/abs/2607.09190v1
• Date: Fri, 10 Jul 2026 08:32:19 GMT
• Title: TactiDex: A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation
• Abstract summary: Current human-to-robot dexterous transfer pipelines rely on kinematic trajectories.<n>We introduce TactiDex, a real-world tactile-guided benchmark to move dexterous manipulation beyond kinematic mimicry.<n>We introduce TactiSkill, a framework built upon a novel tri-component tactile reward that innovatively uses tactile signals as structured supervision.
• Score: 30.144946280089727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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/.

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