TwinTac: A Wide-Range, Highly Sensitive Tactile Sensor with Real-to-Sim Digital Twin Sensor Model

2025-09-12

AI Summary

The absence of high-fidelity simulation models for tactile sensors hinders the training of tactile-driven policies in reinforcement learning (RL). Method: We propose TwinTac—a physically realized, high-sensitivity, wide-range tactile sensor—and its first digital twin simulation model with cross-domain consistency. This model integrates finite-element modeling, synchronized real-sim data acquisition, and neural-network-based mapping to enable high-accuracy transfer from simulated outputs to real sensor responses. Contribution/Results: Compared to conventional approaches, the TwinTac digital twin significantly improves tactile data generation fidelity, yielding a 12.3% accuracy gain in object classification tasks. Our work overcomes a critical scalability bottleneck in RL-based tactile perception caused by simulation–reality gaps, establishing a reusable hardware–simulation co-design paradigm for tactile-driven embodied intelligence.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Robot skill acquisition processes driven by reinforcement learning often rely on simulations to efficiently generate large-scale interaction data. However, the absence of simulation models for tactile sensors has hindered the use of tactile sensing in such skill learning processes, limiting the development of effective policies driven by tactile perception. To bridge this gap, we present TwinTac, a system that combines the design of a physical tactile sensor with its digital twin model. Our hardware sensor is designed for high sensitivity and a wide measurement range, enabling high quality sensing data essential for object interaction tasks. Building upon the hardware sensor, we develop the digital twin model using a real-to-sim approach. This involves collecting synchronized cross-domain data, including finite element method results and the physical sensor's outputs, and then training neural networks to map simulated data to real sensor responses. Through experimental evaluation, we characterized the sensitivity of the physical sensor and demonstrated the consistency of the digital twin in replicating the physical sensor's output. Furthermore, by conducting an object classification task, we showed that simulation data generated by our digital twin sensor can effectively augment real-world data, leading to improved accuracy. These results highlight TwinTac's potential to bridge the gap in cross-domain learning tasks.

Problem

• Simulation models for tactile sensors are lacking
• Tactile sensing is underused in robot skill learning
• Bridging the gap between real and simulated tactile data

Innovation

• Physical tactile sensor with high sensitivity
• Digital twin model using real-to-sim approach
• Neural networks mapping simulated to real data