FlowTouch: View-Invariant Visuo-Tactile Prediction
Overview
This paper introduces FlowTouch, a novel model for view-invariant visuo-tactile prediction that leverages an object's local 3D mesh to encode rich information while abstracting away scene-dependent details. By integrating scene reconstruction and Flow Matching-based models for image generation, FlowTouch bridges the sim-to-real gap and generalizes to new sensor instances. The resulting tactile images can be used for downstream grasp stability prediction. Code, datasets, and videos are available at https://flowtouch.github.io/.
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“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/”
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Artifact availability · dataset: available
“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/”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalFlowTouch: View-Invariant Visuo-Tactile Prediction
This paper introduces FlowTouch, a novel model for view-invariant visuo-tactile prediction that leverages an object's local 3D mesh to encode rich information while abstracting away scene-dependent details. By integrating scene reconstruction and Flow Matching-based models for image generation, FlowTouch bridges the sim-to-real gap and generalizes to new sensor instances. The resulting tactile images can be used for downstream grasp stability prediction. Code, datasets, and videos are available at https://flowtouch.github.io/.
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