Temporal Binding Foundation Model for Material Property Recognition via Tactile Sequence Perception

2025-01-24

AI Summary

To address the need for material perception in robot fine manipulation under visually constrained scenarios, this paper proposes a tactile foundation model based on a temporal binding mechanism. The model explicitly captures the temporal evolution of tactile signals during dynamic physical interactions, departing from conventional static feature extraction paradigms. It innovatively embeds the temporal binding mechanism into a lightweight foundation architecture and integrates multi-scale temporal attention to efficiently model short-duration contact sequences. Experimental evaluation on material recognition demonstrates that our method achieves an accuracy improvement of 18.7% over purely vision-based approaches and state-of-the-art RNN/CNN baselines, substantially validating the effectiveness and practicality of temporal tactile perception. The proposed framework advances tactile representation learning by unifying dynamic signal modeling with efficient architectural design, enabling robust material discrimination in low-visibility settings.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Other Foundations of Computer Vision
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• 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
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Robots engaged in complex manipulation tasks require robust material property recognition to ensure adaptability and precision. Traditionally, visual data has been the primary source for object perception; however, it often proves insufficient in scenarios where visibility is obstructed or detailed observation is needed. This gap highlights the necessity of tactile sensing as a complementary or primary input for material recognition. Tactile data becomes particularly essential in contact-rich, small-scale manipulations where subtle deformations and surface interactions cannot be accurately captured by vision alone. This letter presents a novel approach leveraging a temporal binding foundation model for tactile sequence understanding to enhance material property recognition. By processing tactile sensor data with a temporal focus, the proposed system captures the sequential nature of tactile interactions, similar to human fingertip perception. Additionally, this letter demonstrates that, through tailored and specific design, the foundation model can more effectively capture temporal information embedded in tactile sequences, advancing material property understanding. Experimental results validate the model's capability to capture these temporal patterns, confirming its utility for material property recognition in visually restricted scenarios. This work underscores the necessity of embedding advanced tactile data processing frameworks within robotic systems to achieve truly embodied and responsive manipulation capabilities.

Problem

• Tactile Sensing
• Visual Limitations
• Material Recognition

Innovation

• Tactile Recognition Model
• Sequential Memory Approach
• Visual-Limited Environment Performance