2026-09-19T22:28:11Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/108623 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Zhang, Yang Shen, Zhongyi 2020-10-07T22:44:40Z 2020-10-07T22:44:40Z 2022-10-07T22:44:53Z 2020-07-20 2020-08 This thesis provides a deep reinforcement learning (DRL) based approach for the development of a control policy for a 2D soft robotic arm. The simulation is based on the SOFA framework, which is a real-time multi-physics simulation package capable of creating models and computing forces for deformable materials. The 2D soft robotic arm is composed of two modules where each module consists of two pneumatic actuators and can extend and bend. Though DRL has been explored in the soft robotics realm, end-to-end training has not been developed. Herein, this thesis presents an end-to-end training from snapshots of the simulation to control policy guiding the soft robotic arm to reach a designated target using DRL, and proofs the validity and stability of this approach. The soft robotic arm is able to reach the target with a 98.1% success rate after approximately 30 epochs of training both for fixed initial position training and varying initial position training. The methodology presented here can be generalized for intelligent motion planning and control of soft robotic systems that are otherwise challenging. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-08-01 The student, Zhongyi Shen, accepted the attached license on 2020-07-16 at 13:01. The student, Zhongyi Shen, submitted this Thesis for approval on 2020-07-16 at 13:19. This Thesis was approved for publication on 2020-07-20 at 09:46. DSpace SAF Submission Ingestion Package generated from Vireo submission #15656 on 2020-10-02 at 15:33:43 Made available in DSpace on 2020-10-07T22:44:40Z (GMT). No. of bitstreams: 2 SHEN-THESIS-2020.pdf: 1833666 bytes, checksum: 0805122f691ab6d6484c6d547fadfc18 (MD5) LICENSE.txt: 4209 bytes, checksum: 2fe72af83e28812ad6bf6889555addd4 (MD5) Previous issue date: 2020-07-20 Embargo set by: Seth Robbins for item 116250 Lift date: 2022-10-07T22:44:53Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only application/pdf http://hdl.handle.net/2142/108623 en Copyright 2020 Zhongyi Shen deep Q network deep reinforcement learning soft robotic arm control Deep reinforcement learning control of a 2D soft robotic arm text Thesis Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Thesis M.S.