robotics
One Hundred Hours of Human Touch Improved the Robot's World Model
Shared tactile arrays transferred contact dynamics while real-robot supervision stayed fixed at five hours.
Summary
Shared tactile arrays transferred contact dynamics while real-robot supervision stayed fixed at five hours.
DexTouch-WM places compatible flexible tactile arrays on human and robot hands, retargets human motion into the robot action space, and trains a model to predict future video and bilateral touch. With five hours of robot data held constant, increasing human interaction from zero to 100 hours improved held-out robot-domain visual, geometric and contact prediction even though the human and robot task sets did not overlap. The model also served as a surrogate environment for policy evaluation and generated synthetic trajectories for physical policy learning.
Why it matters
Shared tactile arrays transferred contact dynamics while real-robot supervision stayed fixed at five hours.
Limits and context
- With five hours of robot data held constant, increasing human interaction from zero to 100 hours improved held-out robot-domain visual, geometric and contact prediction even though the human and robot task sets did not overlap.
Key claims
Shared tactile arrays transferred contact dynamics while real-robot supervision stayed fixed at five hours.
Qualification: With five hours of robot data held constant, increasing human interaction from zero to 100 hours improved held-out robot-domain visual, geometric and contact prediction even though the human and robot task sets did not overlap.
Evidence: source-2026-09-18-009
Sources
- arXiv preprint 2609.20649arXiv · primary research
Corrections
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