Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
As an essential modality for dexterous and contact-rich tasks, tactile sensing provides precise force feedback that cannot be reliably inferred from vision. However, limited by hardware and data collection systems, existing datasets with tactility remain small in scale and narrow in contact coverage. Meanwhile, Vision-Language-Action (VLA) models with tactile modality are constrained on dynamics-agnostic post-training, which limits the performance ceiling on downstream tasks. In this paper, we present H-Tac, a large-scale tactile-action dataset with 160-hour egocentric human videos containing
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- LinkedLinked via unknownVideo Friday: AI Gives Robot Hands Humanlike Dexterity →
- LinkedLinked via arxiv author · 85%Chi Zhang →
“Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation”
- LinkedLinked via arxiv author · 85%Penglin Cai →
“Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation”
- LinkedLinked via arxiv author · 85%Ziheng Xi →
“Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation”
- LinkedLinked via arxiv author · 85%Haoqi Yuan →
“Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation”
- LinkedLinked via arxiv author · 85%Hao Luo →
“Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation”
- LinkedLinked via arxiv author · 85%Wanpeng Zhang →
“Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation”
- LinkedLinked via arxiv author · 85%Sipeng Zheng →
“Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation”