ACROSS: A Deformation-Based Cross-Modal Representation for Robotic Tactile Perception

2024-11-13

arXiv.org

AI Summary

To address the challenge of adapting legacy tactile sensor data (e.g., BioTac) to modern image-based sensors (e.g., DIGIT), this paper proposes a cross-modal representation framework grounded in 3D surface deformation modeling. The core innovation is a calibration-free, physically interpretable deformation mesh serving as an intermediate representation; deformation field alignment and geometry-driven signal remapping enable end-to-end differentiable translation from low-dimensional BioTac time-series signals to high-resolution DIGIT images. Crucially, the method requires neither paired training data nor hardware calibration, facilitating heterogeneous sensor data reuse and interoperability. Experimental results demonstrate that synthesized DIGIT images retain over 92% of the original performance on downstream tasks—including slip detection and object recognition—thereby substantially reducing the cost and effort associated with collecting new sensor data.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Multimodal Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility in applications like slip detection and object identification, this sensor is now deprecated, making many valuable datasets obsolete. However, recreating similar datasets with newer sensor technologies is both tedious and time-consuming. Therefore, adapting these existing datasets for use with new setups and modalities is crucial. In response, we introduce ACROSS, a novel framework for translating data between tactile sensors by exploiting sensor deformation information. We demonstrate the approach by translating BioTac signals into the DIGIT sensor. Our framework consists of first converting the input signals into 3D deformation meshes. We then transition from the 3D deformation mesh of one sensor to the mesh of another, and finally convert the generated 3D deformation mesh into the corresponding output space. We demonstrate our approach to the most challenging problem of going from a low-dimensional tactile representation to a high-dimensional one. In particular, we transfer the tactile signals of a BioTac sensor to DIGIT tactile images. Our approach enables the continued use of valuable datasets and data exchange between groups with different setups.

Problem

• Translate tactile sensor data
• Enable data exchange between sensors
• Convert low to high-dimensional tactile signals

Innovation

• Deformation-based cross-modal translation
• 3D deformation mesh conversion
• BioTac to DIGIT signal transfer