Biomimetic Evaluation of an Underwater Soft Hand Through Deep Learning-based 3D Pose Reconstruction

TL;DR

This paper introduces a deep learning-based 3D pose reconstruction method for a soft robotic hand, enabling accurate analysis of grasping motions using multi-view videos and improving understanding of soft robot-human motion similarities.

Contribution

The study presents a novel approach combining deep learning and multi-view imaging for 3D pose estimation of soft robotic hands, enhancing motion analysis accuracy.

Findings

Soft robotic hand exhibits human-like grasping motions.

Deep learning models effectively reconstruct 3D finger poses.

The method achieves satisfactory accuracy in pose reconstruction.

Abstract

Soft robotic hand shows considerable promise for various grasping applications. However, the sensing and reconstruction of the robot pose will cause limitation during the design and fabrication. In this work, we present a novel 3D pose reconstruction approach to analyze the grasping motion of a bidirectional soft robotic hand using experiment videos. The images from top, front, back, left, right view were collected using an one-camera-multiple-mirror imaging device. The coordinate and orientation information of soft fingers are detected based on deep learning methods. Faster RCNN model is used to detect the position of fingertips, while U-Net model is applied to calculate the side boundary of the fingers. Based on the kinematics, the corresponding coordinate and orientation databases are established. The 3D pose reconstructed result presents a satisfactory performance and good accuracy. Using efficacy coefficient method, the finger contribution of the bending angle and distance between fingers of soft robot hand is analyzed by compared with that of human hand. The results show that the soft robot hand perform a human-like motion in both single-direction and bidirectional grasping.