TactAlign: Human-to-Robot Policy Transfer via Tactile Alignment
Human demonstrations collected by wearable devices (e.g., tactile gloves) provide fast and dexterous supervision for policy learning, and are guided by rich, natural tactile feedback. However, a key challenge is how to transfer human-collected tactile signals to robots despite the differences in sensing modalities and embodiment. Existing human-to-robot (H2R) approaches that incorporate touch often assume identical tactile sensors, require paired data, and involve little to no embodiment gap between human demonstrator and the robots, limiting scalability and generality. We propose TactAlign, a cross-embodiment tactile alignment method that transfers human-collected tactile signals to a robot with different embodiment. TactAlign transforms human and robot tactile observations into a shared latent representation using a rectified flow, without paired datasets, manual labels, or privileged information. Our method enables low-cost latent transport guided by hand-object interaction-derived pseudo-pairs. We demonstrate that TactAlign improves H2R policy transfer across multiple contact-rich tasks (pivoting, insertion, lid closing), generalizes to unseen objects and tasks with human data (less than 5 minutes), and enables zero-shot H2R transfer on a highly dexterous tasks (light bulb screwing).
6996a91e1268a6b79e0d033f
Youngsun Wi
claimed_verified
2026-02-19T09:51:39.647Z
626c8c89a317717549e4e5aa
/avatars/b8bf54005b7b2a8fa0d10ed4557b675d.svg
youngsun wi
youngw
user
youngw
6996a91e1268a6b79e0d0340
Jessica Yin
admin_assigned
2026-02-20T09:08:58.693Z
670205302fa99176353ffa86
/avatars/1a9f6d16c9b69f452574f203cfbb8413.svg
Jessica Yin
Jessicayin
user
Jessicayin
6996a91e1268a6b79e0d0341
Elvis Xiang
admin_assigned
2026-02-20T09:09:04.921Z
68d8104df85bf039b0fb80aa
/avatars/41360c5db804c9940a5a2b30b49327ef.svg
Elvis Xiang
elvisxiang
user
elvisxiang
6996a91e1268a6b79e0d0342
Akash Sharma
6996a91e1268a6b79e0d0343
Jitendra Malik
admin_assigned
2026-02-20T09:09:17.040Z
65369a95605a07338de78ab0
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/sGFjOjLT2akN-sn5beVWL.jpeg
Jitendra Malik 
jitendra1995
user
jitendra1995
6996a91e1268a6b79e0d0344
Mustafa Mukadam
6996a91e1268a6b79e0d0345
Nima Fazeli
admin_assigned
2026-02-20T09:09:26.108Z
66919fa1306a261f1850b27e
/avatars/34d7956986bef23d01c2bd4ebd4e9765.svg
Nima Fazeli
nimafazeli
user
nimafazeli
6996a91e1268a6b79e0d0346
Tess Hellebrekers
admin_assigned
2026-02-20T09:09:32.523Z
67a3e8063dd995045edcc833
/avatars/58d7fbade6a900a8748a245df5e5338e.svg
Tess
tesshellebrekers
user
tesshellebrekers
TactAlign: Human-to-Robot Policy Transfer via Tactile Alignment
Human demonstrations collected by wearable devices (e.g., tactile gloves) provide fast and dexterous supervision for policy learning, and are guided by rich, natural tactile feedback. However, a key challenge is how to transfer human-collected tactile signals to robots despite the differences in sensing modalities and embodiment. Existing human-to-robot (H2R) approaches that incorporate touch often assume identical tactile sensors, require paired data, and involve little to no embodiment gap between human demonstrator and the robots, limiting scalability and generality. We propose TactAlign, a cross-embodiment tactile alignment method that transfers human-collected tactile signals to a robot with different embodiment. TactAlign transforms human and robot tactile observations into a shared latent representation using a rectified flow, without paired datasets, manual labels, or privileged information. Our method enables low-cost latent transport guided by hand-object interaction-derived pseudo-pairs. We demonstrate that TactAlign improves H2R policy transfer across multiple contact-rich tasks (pivoting, insertion, lid closing), generalizes to unseen objects and tasks with human data (less than 5 minutes), and enables zero-shot H2R transfer on a highly dexterous tasks (light bulb screwing).
TactAlign enables transfer of human tactile demonstrations to robots with different embodiments through cross-embodiment tactile alignment without requiring paired data or manual labels.
human-to-robot transfer tactile alignment rectified flow latent representation pseudo-pairs contact-rich tasks zero-shot transfer