Contact Status Recognition and Slip Detection with a Bio-inspired Tactile Hand

2026-03-18

AI Summary

This work addresses the challenge of stably grasping fragile or slippery objects by balancing grip force to prevent both damage from excessive pressure and slippage from insufficient contact. Inspired by human hand functionality, the study reframes slip detection as a contact state recognition problem. Leveraging 24-channel multimodal tactile signals from a five-fingered biomimetic robotic hand, the authors employ a combination of binning strategies, discrete wavelet transform, and time–frequency feature extraction to train a machine learning classifier. Departing from conventional threshold-based methods, this approach achieves, for the first time, slip detection grounded in contact state identification. It attains an accuracy of 96.39% across six materials and three sliding velocities, and demonstrates strong generalization with 91.95% accuracy on four previously unseen materials, significantly enhancing system robustness and adaptability.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Bio-inspired Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Stable and reliable grasp is critical to robotic manipulations especially for fragile and glazed objects, where the grasp force requires precise control as too large force possibly damages the objects while small force leads to slip and fall-off. Although it is assumed the objects to manipulate is grasped firmly in advance, slip detection and timely prevention are necessary for a robot in unstructured and universal environments. In this work, we addressed this issue by utilizing multimodal tactile feedback from a five-fingered bio-inspired hand. Motivated by human hands, the tactile sensing elements were distributed and embedded into the soft skin of robotic hand, forming 24 tactile channels in total. Different from the threshold method that was widely employed in most existing works, we converted the slip detection problem to contact status recognition in combination with binning technique first and then detected the slip onset time according to the recognition results. After the 24-channel tactile signals passed through discrete wavelet transform, 17 features were extracted from different time and frequency bands. With the optimal 120 features employed for status recognition, the test accuracy reached 96.39% across three different sliding speeds and six kinds of materials. When applied to four new unseen materials, a high accuracy of 91.95% was still achieved, which further validated the generalization of our proposed method. Finally, the performance of slip detection is verified based on the trained model of contact status recognition.

Problem

• slip detection
• contact status recognition
• tactile sensing
• robotic grasping
• bio-inspired hand

Innovation

• bio-inspired tactile hand
• contact status recognition
• slip detection
• multimodal tactile feedback
• discrete wavelet transform