ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Soft Robotics
Slip Detection for Compliant Robotic Hands Using Inertial Signals and Deep Learning
Provisionally accepted
- Oregon State University, Corvallis, United States
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When a passively compliant hand grasps an object, slip events are often accompanied by flexion or extension of the finger or finger joints. This paper investigates whether a combination of orientation change and slip-induced vibration at the fingertip, as sensed by an inertial measurement unit (IMU), can be used as a slip indicator. Using a tendon-driven hand, which achieves passive compliance through underactuation, we performed 195 manipulation trials involving both slip and non-slip conditions. We then labeled this data automatically using motion-tracking data, and trained a convolutional neural network (CNN) to detect the slip events. Our results show that slip can be successfully detected from IMU data, even in the presence of other disturbances. This remains the case when deploying the trained network on data from a different gripper performing a new manipulation task on a previously unseen object.
Keywords: compliant hand, slip detection, Inertial measurement unit, grasping, Contact-rich Manipulation
Received: 03 Sep 2025; Accepted: 26 Nov 2025.
Copyright: © 2025 Cravetz, Vyas, Grimm and Davidson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Miranda Cravetz
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