aiqbench analysis
This arXiv paper proposes a learned generative model that predicts tactile sensor images from camera views by conditioning on an object's local 3D mesh, using Flow Matching. The reported gains are sim-to-real transfer, generalization to new sensor instances, and use of the predicted tactile images for grasp stability prediction. For ML readers, the main question is whether the mesh-based abstraction holds up beyond the authors' own sensors and datasets, which the released code and data should let others test.
FlowTouch: View-Invariant Visuo-Tactile Prediction
arXiv:2603.08255v2 Announce Type: replace-cross Abstract: Tactile sensation is essential for contact-rich manipulation tasks. It provides direct feedback on object geometry, surface properties, and interaction forces, enhancing perception and enabling fine-grained control. An inherent limitation of tactile sensors is that readings are available only when an object is touched. This precludes their use during planning and the initial execution phase of a task. Predicting tactile information from visual information can bridge this gap. A common approach is to learn a direct mapping from camera images to the output of vision-based tactile sensors. However, the resulting model will depend strongly on the specific setup and on how well the camera can capture the area where an object is touched. In this work, we introduce FlowTouch, a novel model for view-invariant visuo-tactile prediction. Our key idea is to use an object's local 3D mesh to encode rich information for predicting tactile patterns while abstracting away from scene-dependent details. FlowTouch integrates scene reconstruction and Flow Matching-based models for image generation. Our results show that FlowTouch is able to bridge the sim-to-real gap and generalize to new sensor instances. We further show that the resulting tactile images can be used for downstream grasp stability prediction. Our code, datasets and videos are available at https://flowtouch.github.io/
Abstract:Tactile sensation is essential for contact-rich manipulation tasks. It provides direct feedback on object geometry, surface properties, and interaction forces, enhancing perception and enabling fine-grained control. An inherent limitation of tactile sensors is that readings are available only when an object is touched. This precludes their use during planning and the initial execution phase of a task. Predicting tactile information from visual information can bridge this gap. A common approach is to learn a direct mapping from camera images to the output of vision-based tactile sensors. However, the resulting model will depend strongly on the specific setup and on how well the camera can capture the area where an object is touched. In this work, we introduce FlowTouch, a novel model for view-invariant visuo-tactile prediction. Our key idea is to use an object's local 3D mesh to encode rich information for predicting tactile patterns while abstracting away from scene-dependent details. FlowTouch integrates scene reconstruction and Flow Matching-based models for image generation. Our results show that FlowTouch is able to bridge the sim-to-real gap and generalize to new sensor instances. We further show that the resulting tactile images can be used for downstream grasp stability prediction. Our code, datasets and videos are available at this https URL
Robotics (cs.RO); Machine Learning (cs.LG)
arXiv:2603.08255 [cs.RO]
(or arXiv:2603.08255v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.08255 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 300 words