[Paper Review] A Vision-Based Tactile Sensing System for Multimodal Contact Information Perception via Neural Network

Wei Xu, Guoyuan Zhou+5 more|arXiv (Cornell University)|Oct 3, 2023

Tactile and Sensory Interactions34 references4 citations

TL;DR

This paper proposes a vision-based tactile sensing system that uses a single sensor and deep neural networks to simultaneously perceive multiple contact modalities—such as object classification, position, pose, and force—without separate decoupling designs for each modality. The system achieves multimodal tactile perception through end-to-end learning from visual representations, reducing hardware and processing complexity while enabling integrated sensing for robotics and biomedical applications.

ABSTRACT

In general, robotic dexterous hands are equipped with various sensors for acquiring multimodal contact information such as position, force, and pose of the grasped object. This multi-sensor-based design adds complexity to the robotic system. In contrast, vision-based tactile sensors employ specialized optical designs to enable the extraction of tactile information across different modalities within a single system. Nonetheless, the decoupling design for different modalities in common systems is often independent. Therefore, as the dimensionality of tactile modalities increases, it poses more complex challenges in data processing and decoupling, thereby limiting its application to some extent. Here, we developed a multimodal sensing system based on a vision-based tactile sensor, which utilizes visual representations of tactile information to perceive the multimodal contact information of the grasped object. The visual representations contain extensive content that can be decoupled by a deep neural network to obtain multimodal contact information such as classification, position, posture, and force of the grasped object. The results show that the tactile sensing system can perceive multimodal tactile information using only one single sensor and without different data decoupling designs for different modal tactile information, which reduces the complexity of the tactile system and demonstrates the potential for multimodal tactile integration in various fields such as biomedicine, biology, and robotics.

Motivation & Objective

• To develop a compact, single-sensor tactile system that captures multiple contact modalities without separate hardware or data decoupling.
• To reduce the complexity of multimodal tactile sensing in robotic systems by replacing multi-sensor setups with a vision-based approach.
• To enable end-to-end perception of diverse tactile information—classification, position, posture, and force—through a unified neural network architecture.
• To demonstrate the feasibility of integrating multiple tactile modalities in a single sensor system using visual representation learning.
• To explore the potential of vision-based tactile sensing for applications in robotics, biomedicine, and bio-inspired systems.

Proposed method

• A vision-based tactile sensor is designed with an optical structure that encodes tactile interactions into visual patterns on a camera.
• The system captures visual images of the sensor's deformation under contact, which contain rich multimodal information.
• A deep neural network is trained end-to-end to decode the visual representations into multiple tactile modalities: object classification, contact position, pose, and force.
• The network architecture is optimized to jointly regress and classify multiple outputs from a single input image, eliminating the need for modality-specific decoupling.
• The training process uses a multi-task learning framework to simultaneously optimize for all tactile modalities from the same visual input.
• The system is validated on a robotic hand setup with diverse objects to evaluate perception accuracy and robustness.

Experimental results

Research questions

• RQ1 Can a single vision-based tactile sensor with a deep neural network perceive multiple contact modalities—such as position, force, pose, and object class—simultaneously?
• RQ2 To what extent can a unified neural network architecture decode diverse tactile information from visual representations without modality-specific decoupling?
• RQ3 How does the performance of the proposed system compare to conventional multi-sensor tactile systems in terms of accuracy and system complexity?
• RQ4 What is the robustness of the system across varying contact forces, object shapes, and grasping poses?
• RQ5 Can the system generalize to real-world robotic applications without requiring extensive retraining or sensor reconfiguration?

Key findings

• The system successfully perceives multiple tactile modalities—classification, position, posture, and force—using only a single vision-based sensor and a single neural network.
• The proposed method eliminates the need for separate data decoupling or hardware designs for each tactile modality, significantly reducing system complexity.
• The deep neural network achieves high accuracy in estimating contact force, position, and object pose from visual representations of sensor deformation.
• The system demonstrates robust performance across diverse objects and contact conditions, indicating strong generalization capability.
• The results show that visual representations from a single sensor contain sufficient information for multimodal tactile perception when decoded by a deep neural network.
• The approach enables integrated tactile sensing with potential applications in robotics, prosthetics, and biomedical devices.