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This paper presents a novel approach to slip detection in robotic hands using multimodal tactile feedback, addressing the challenge of maintaining stable grasps on fragile objects. The method converts slip detection into a contact status recognition problem, leveraging a bio-inspired five-fingered hand with 24 tactile channels. By applying discrete wavelet transform and extracting 120 optimal features, the system achieves a 96.39% accuracy in recognizing contact status across various materials and sliding speeds, demonstrating strong generalization to unseen materials (91.95% accuracy).
Ditch the threshold: this tactile-sensing robotic hand uses contact status recognition to detect slip with 96% accuracy, even on new materials.
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.
