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SoTa: Soft Tactile Skins for Dexterous Manipulation

SoTa: Soft Tactile Skins for Dexterous Manipulation

Jingyun Yang, Baiyu Shi, Timothy Yu, Haitian Liu, Alberta Longhini, Weichen Wang, Rika Antonova, Zhenan Bao, Jeannette Bohg · N/A · 2026

manipulation vision reinforcement-learning learning-from-demonstration tactile benchmark
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Summary

A growing body of work suggests that tactile sensing gives robot policies contact information that complements vision in dexterous manipulation. However, visuo-tactile robot data remains scarce: dexterous demonstrations require teleoperating robots, which limits dataset scale.

Abstract Summary

A growing body of work suggests that tactile sensing gives robot policies contact information that complements vision in dexterous manipulation. However, visuo-tactile robot data remains scarce: dexterous demonstrations require teleoperating robots, which limits dataset scale. Human demonstrations are far cheaper to collect and offer a path to scale this data, but only if human and robot hands carry tactile sensors with corresponding signals. This requires sensors that conform to different hand geometries, cover the full hand, and share a common layout across embodiments. We present SoTa, a low-cost capacitive tactile skin that provides full-hand coverage on humans and robots while preserving a shared layout of 202 taxels across corresponding finger and palm regions. Our multilayer design with fabric electrodes enables in-house fabrication of thin, soft skins with customizable geometry for under $10 in materials per skin. The sensor retains over 97% of its initial response span after 10,000 loading-unloading cycles with traces retaining continuity through 1,280 tight-fist folding cycles. The shared taxel layout supports human-robot co-training with a common tactile encoder and no learned cross-sensor mapping. Across three contact-rich manipulation tasks, tactile observations improve in-distribution success over vision-only policies. With a fixed robot demonstration budget, adding human demonstrations more than doubles mean success across eight evaluation conditions, from 22.8% to 45.9%, improving success in all five out-of-distribution conditions. We plan to open-source the resources needed to fabricate and operate these skins.

Key Points

  • A growing body of work suggests that tactile sensing gives robot policies contact information that complements vision in dexterous manipulation.
  • However, visuo-tactile robot data remains scarce: dexterous demonstrations require teleoperating robots, which limits dataset scale.
  • Human demonstrations are far cheaper to collect and offer a path to scale this data, but only if human and robot hands carry tactile sensors with corresponding signals.
  • This requires sensors that conform to different hand geometries, cover the full hand, and share a common layout across embodiments.
  • We present SoTa, a low-cost capacitive tactile skin that provides full-hand coverage on humans and robots while preserving a shared layout of 202 taxels across corresponding finger and palm regions.

The SoTa Sensor

We describe our SoTa sensor design (Section III-A ), our visuo-tactile dexterous manipulation data collection setup, which supports human and robot demonstration collection (Section III-B ), and our policy learning architecture, with a shared tactile encoder across embodiments (Section III-C ). (a) (b) (c) (d) We build tactile skins for both the human demonstrator and the robot hand.

The devices are mechanically customized to each embodiment but share the same capacitive sensing principle, taxel size, and taxel count.

The SoTa Sensor — SoTa: Soft Tactile Skins for Dexterous Manipulation
Fig. 2 : SoTa tactile skin hardware. (a) Robot (left) and human (right) skins with custom-fit form factors and matched region-level taxel layouts. (b) Exploded view showing stack-up of the multilayer capacitive sensor. (c) Sensor responses over three consecutive cycles per load from 20 mN to 10 N; boxed inset shows the 20 mN detail. (d) Sensor capacitance over 10,000 loading-unloading cycles with a peak force of 8 N. The dense band’s upper and lower edges represent loaded and unloaded sensor outputs; the approximately rectangular band indicates stable cyclic response.

Experimental Evaluation

We evaluate three aspects of SoTa. First, we test whether tactile observations improve contact-rich manipulation relative to vision-only policies. Second, we examine the effects of auxiliary language supervision and human-robot co-training under a fixed robot data budget. Finally, we characterize sensor durability and motion-induced artifacts. Tasks.

Experimental Evaluation — SoTa: Soft Tactile Skins for Dexterous Manipulation
Fig. 3 : Our data collection setup. (a) Robot teleoperation. A Meta Quest controller tracks the operator’s wrist pose and a Rokoko smart glove tracks per-finger joints; both streams are fused at 30 Hz and command the dexterous Kinova + Wuji hand setup. A front-facing ZED Mini camera and our tactile skins record synchronized observations. (b) Human demonstration collection. The operator wears a Rokoko glove and our human-fit tactile skin while performing the same tasks without the robot. Human hand configurations were recorded for an exploratory hand pose prediction objective but are not used in our method or the reported experiments.

Sources and Demonstrations

Figures are reproduced from Yang et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.

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