Abstract
Contact-rich policies often fail because distinct physical states look alike yet require different actions. Cameras may not reveal whether a connector is aligned or fully seated, while many normal-only tactile sensors can miss the tangential interactions perpendicular to the grasping direction that distinguish these states. We ask whether a learning policy needs calibrated shear measurements, or only a repeatable observation that separates shear-dependent contact states. We introduce TacGooseBumps (TacGB), a passive domed film that mechanically encodes tangential loading as pattern changes in an existing sensor's pressure map. Tangential loading tilts each dome and redistributes pressure across its footprint; an end-to-end policy consumes the resulting maps without added electronics, force reconstruction, or taxel-level dome alignment. Across four imitation-learning tasks and two data-collection pipelines, TacGB improves goal attainment, efficiency, and contact quality: insertion success increases by up to 36 percentage points, and successful insertions are completed faster, while fragile-object placement becomes gentler and drawing becomes more continuous and straight. Signal, stage-wise, failure-mode, and trajectory analyses link these gains to contact regimes in which task-relevant tangential interactions are poorly resolved by vision and normal pressure alone. Together, these results show that shear need not be measured metrically to benefit robot learning; it can instead be mechanically encoded without changing the underlying tactile sensor or the policy's pressure-map input format.
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Cite this article
APA 7
Li, W., Yang, B., Chen, Y., Wang, A., & Tomizuka, M. (2026). TacGooseBumps (TacGB): Retrofitting Normal-Only Tactile Sensors with Shear Encoding for Learning Contact-Rich Manipulation. https://omanscience.com/en/articles/tacgoosebumps-tacgb-retrofitting-normal-only-tactile-sensors-with-shear-encoding-for-learning-contact-rich-manipulation
MLA 9
Li, Wenjie, et al. "TacGooseBumps (TacGB): Retrofitting Normal-Only Tactile Sensors with Shear Encoding for Learning Contact-Rich Manipulation." https://omanscience.com/en/articles/tacgoosebumps-tacgb-retrofitting-normal-only-tactile-sensors-with-shear-encoding-for-learning-contact-rich-manipulation.
Chicago (author–date)
Li, Wenjie, Binyu Yang, Yuxin Chen, Ambrose Wang, and Masayoshi Tomizuka. 2026. "TacGooseBumps (TacGB): Retrofitting Normal-Only Tactile Sensors with Shear Encoding for Learning Contact-Rich Manipulation." https://omanscience.com/en/articles/tacgoosebumps-tacgb-retrofitting-normal-only-tactile-sensors-with-shear-encoding-for-learning-contact-rich-manipulation.
Harvard
Li, W., Yang, B., Chen, Y., Wang, A. and Tomizuka, M. (2026) 'TacGooseBumps (TacGB): Retrofitting Normal-Only Tactile Sensors with Shear Encoding for Learning Contact-Rich Manipulation', Available at: https://omanscience.com/en/articles/tacgoosebumps-tacgb-retrofitting-normal-only-tactile-sensors-with-shear-encoding-for-learning-contact-rich-manipulation.
Vancouver
Li W, Yang B, Chen Y, Wang A, Tomizuka M. TacGooseBumps (TacGB): Retrofitting Normal-Only Tactile Sensors with Shear Encoding for Learning Contact-Rich Manipulation. https://omanscience.com/en/articles/tacgoosebumps-tacgb-retrofitting-normal-only-tactile-sensors-with-shear-encoding-for-learning-contact-rich-manipulation
IEEE
W. Li, B. Yang, Y. Chen, A. Wang, and M. Tomizuka, "TacGooseBumps (TacGB): Retrofitting Normal-Only Tactile Sensors with Shear Encoding for Learning Contact-Rich Manipulation," https://omanscience.com/en/articles/tacgoosebumps-tacgb-retrofitting-normal-only-tactile-sensors-with-shear-encoding-for-learning-contact-rich-manipulation.