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1. Contrastive touch-to-touch pretraining

   Rodriguez, Samanta; Dou, Yiming; van_den_Bogert, William; Oller, Miquel; So, Kevin; Owens, Andrew; Fazeli, Nima (May 2025, International Conference on Robotics and Automation (ICRA))

   Today’s tactile sensors have a variety of different designs, making it challenging to develop general-purpose meth- ods for processing touch signals. In this paper, we learn a unified representation that captures the shared information between different tactile sensors. Unlike current approaches that focus on reconstruction or task-specific supervision, we leverage contrastive learning to integrate tactile signals from two different sensors into a shared embedding space, using a dataset in which the same objects are probed with multiple sensors. We apply this approach to paired touch signals from GelSlim and Soft Bubble sensors. We show that our learned features provide strong pretraining for downstream pose estimation and classification tasks. We also show that our embedding enables models trained using one touch sensor to be deployed using another without additional training. 

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2. Contrastive Touch-to-Touch Pretraining

   https://doi.org/10.1109/ICRA55743.2025.11127980  

   Rodriguez, Samanta; Dou, Yiming; van_den_Bogert, William; Oller, Miquel; So, Kevin; Owens, Andrew; Fazeli, Nima (May 2025, IEEE)
3. Quantifying Touch: New Metrics for Characterizing What Happens During a Touch

   https://doi.org/10.1145/3517428.3544804  

   Kong, Junhan; Zhong, Mingyuan; Fogarty, James; Wobbrock, Jacob O. (October 2022, Proceedings of the ACM Conference on Computers and Accessibility)
4. Establishing Connection through Touch: Understanding Affective Communication with Mediated Social Touch

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5. Whose Touch is This? : Understanding the Agency Trade-Off Between User-Driven Touch vs. Computer-Driven Touch

   https://doi.org/10.1145/3489608  

   Tajima, Daisuke; Nishida, Jun; Lopes, Pedro; Kasahara, Shunichi (June 2022, ACM Transactions on Computer-Human Interaction)

   Force-feedback enhances digital touch by enabling users to share non-verbal aspects such as rhythm, poses, and so on. To achieve this, interfaces actuate the user’s to touch involuntarily (using exoskeletons or electrical-muscle-stimulation); we refer to this as computer-driven touch. Unfortunately, forcing users to touch causes a loss of their sense of agency. While researchers found that delaying the timing of computer-driven touch preserves agency, they only considered the naïve case when user-driven touch is aligned with computer-driven touch. We argue this is unlikely as it assumes we can perfectly predict user-touches. But, what about all the remainder situations: when the haptics forces the user into an outcome they did not intend or assists the user in an outcome they would not achieve alone? We unveil, via an experiment, what happens in these novel situations. From our findings, we synthesize a framework that enables researchers of digital-touch systems to trade-off between haptic-assistance vs. sense-of-agency. 

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