Deep Learning-Based Ensemble Approach for Autonomous Object Manipulation with an Anthropomorphic Soft Robot Hand

Author:

Valarezo Añazco Edwin1ORCID,Guerrero Sara2,Rivera Lopez Patricio3,Oh Ji-Heon3,Ryu Ga-Hyeon3,Kim Tae-Seong4ORCID

Affiliation:

1. Faculty in Electricity and Computation FIEC, Escuela Superior Politecnica del Litoral ESPOL, Guayaquil 090202, Ecuador

2. Faculty of Architecture and Design, Universidad Espiritu Santo UEES, Samborondón 092301, Ecuador

3. Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Republic of Korea

4. Department of Biomedical Engineering, Kyung Hee University, Yongin 17104, Republic of Korea

Abstract

Autonomous object manipulation is a challenging task in robotics because it requires an essential understanding of the object’s parameters such as position, 3D shape, grasping (i.e., touching) areas, and orientation. This work presents an autonomous object manipulation system using an anthropomorphic soft robot hand with deep learning (DL) vision intelligence for object detection, 3D shape reconstruction, and object grasping area generation. Object detection is performed using Faster-RCNN and an RGB-D sensor to produce a partial depth view of the objects randomly located in the working space. Three-dimensional object shape reconstruction is performed using U-Net based on 3D convolutions with bottle-neck layers and skip connections generating a complete 3D shape of the object from the sensed single-depth view. Then, the grasping position and orientation are computed based on the reconstructed 3D object information (e.g., object shape and size) using U-Net based on 3D convolutions and Principal Component Analysis (PCA), respectively. The proposed autonomous object manipulation system is evaluated by grasping and relocating twelve objects not included in the training database, achieving an average of 95% successful object grasping and 93% object relocations.

Funder

Korea government

Industrial Technology Innovation Program

Publisher

MDPI AG

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