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This study empirically isolates the contribution of specific physical cues—such as marker flow, geometry, and contact appearance—in improving imitation learning for robots equipped with vision-based tactile sensors. By leveraging a controlled real-robot setup, the research demonstrates that while tactile perception significantly boosts task success rates, different physical signals require distinct processing strategies to be most effective. The findings provide actionable insights for optimizing sensor fusion architectures in contact-rich robotic manipulation tasks.
Which Physical Tactile Cues Matter for Contact-Rich Imitation Learning? A Controlled Real-Robot Study
arXiv:2611.06673v1 Announce Type: new Abstract: Vision-based tactile sensors capture multiple aspects of physical interaction within a single image stream, including contact appearance, geometry, indentation depth, and tangential deformation. However, existing visuo-tactile policies often directly process tactile observations as images, making it difficult to determine which physical cues contribute to policy learning. This paper presents a controlled real-robot study that compares physically interpretable tactile signals using the LeRobot platform, enhanced with our visuo-tactile perception system. From the same tactile stream, we derive tactile RGB images, normal/depth maps, and marker-flow fields, representing contact appearance, geometry, and tangential deformation, respectively. Using identical task demonstrations and the same policy backbone, we evaluate the individual and combined contributions of these tactile cues. We further compare tactile encoders and visuo-tactile fusion strategies. Evaluation across 720 real-robot trials yields four key findings: (1) tactile perception substantially improves the overall success rate from 57.8% to 78.9%, although its contribution varies across task stages; (2) marker flow is the most effective individual cue, achieving a success rate of 74.4% and playing a particularly important role in regulating established contact; (3) cue-factorized encoding consistently improves performance, highlighting that different physical cues require tailored processing techniques; and (4) although the effectiveness of tactile fusion is task-dependent, cross-attention offers a lightweight solution with performance comparable to that of pretrained tactile encoders.
Title:Which Physical Tactile Cues Matter for Contact-Rich Imitation Learning? A Controlled Real-Robot Study
Abstract:Vision-based tactile sensors capture multiple aspects of physical interaction within a single image stream, including contact appearance, geometry, indentation depth, and tangential deformation. However, existing visuo-tactile policies often directly process tactile observations as images, making it difficult to determine which physical cues contribute to policy learning. This paper presents a controlled real-robot study that compares physically interpretable tactile signals using the LeRobot platform, enhanced with our visuo-tactile perception system. From the same tactile stream, we derive tactile RGB images, normal/depth maps, and marker-flow fields, representing contact appearance, geometry, and tangential deformation, respectively. Using identical task demonstrations and the same policy backbone, we evaluate the individual and combined contributions of these tactile cues. We further compare tactile encoders and visuo-tactile fusion strategies. Evaluation across 720 real-robot trials yields four key findings: (1) tactile perception substantially improves the overall success rate from 57.8% to 78.9%, although its contribution varies across task stages; (2) marker flow is the most effective individual cue, achieving a success rate of 74.4% and playing a particularly important role in regulating established contact; (3) cue-factorized encoding consistently improves performance, highlighting that different physical cues require tailored processing techniques; and (4) although the effectiveness of tactile fusion is task-dependent, cross-attention offers a lightweight solution with performance comparable to that of pretrained tactile encoders.
6 pages, 5 figures, 2 tables. Accepted to IEEE ROBIO 2026
Robotics (cs.RO)
arXiv:2611.06673 [cs.RO]
(or arXiv:2611.06673v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.06673 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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