Reproduce “TACROSS: An Efficient and Low-Cost Scalable Human Touch System Across Heterogeneous Tactile Sensors for Dexterous Robot Learning” on OpenResearch
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About this paper
Bo Chen, Huanzhang Hu, Junyang Ma, Bo Yue, Fangdi Yu, Haijier Chen, Xianxin Lai, Shuyu Pan and 6 more
TACROSS aligns human and robot touch by mapping sensor readings into shared contact fields and selecting semantically compatible windows without synchronized frames. Its policy uses executed robot actions as ground-truth supervision, while human data provide tactile evidence. In a four-task comparison, Vision-IK Ego recorded 140 episodes/hour versus 40, with 91.5% mean success versus 92.2%; this supports practical collection, not eliminating robot demonstrations.
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