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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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorChi Zhang →
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
- Linked via arxiv authorPenglin Cai →
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
- Linked via arxiv authorZiheng Xi →
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
- Linked via arxiv authorHaoqi Yuan →
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
- Linked via arxiv authorHao Luo →
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
- Linked via arxiv authorWanpeng Zhang →
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
- Linked via arxiv authorSipeng Zheng →
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
- Linked via arxiv authorChaoyi Xu →
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
- Linked via arxiv authorZongqing Lu →
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation