# Slip detection for compliant robotic hands using inertial signals and deep learning

## TL;DR

This paper explores using inertial signals and deep learning to detect slips in compliant robotic hands.

## Contribution

A novel method for slip detection using IMU data and a CNN, validated across different grippers and objects.

## Key findings

- Slip events can be detected from IMU data using a CNN.
- The method works even with disturbances and on new objects and grippers.

## Abstract

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.