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Correction published on 28 October 2019, see Robotics 2019, 8(4), 93.
Open AccessReview

Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges

by Sarthak Bhagat 1,2,†, Hritwick Banerjee 1,3,†, Zion Tsz Ho Tse 4 and Hongliang Ren 1,3,5,*
Department of Biomedical Engineering, Faculty of Engineering, 4 Engineering Drive 3, National University of Singapore, Singapore 117583, Singapore
Department of Electronics and Communications Engineering, Indraprastha Institute of Information Technology, New Delhi 110020, India
Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore 117456, Singapore
School of Electrical & Computer Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
National University of Singapore (Suzhou) Research Institute (NUSRI), Suzhou Industrial Park, Suzhou 215123, China
Author to whom correspondence should be addressed.
These authors equally contributed towards this manuscript.
Robotics 2019, 8(1), 4;
Received: 30 September 2018 / Revised: 29 December 2018 / Accepted: 1 January 2019 / Published: 18 January 2019
(This article belongs to the Special Issue Cloud Robotics)
The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to the sprouting of a relatively new yet rewarding sphere of technology in intelligent soft robotics. The fusion of deep reinforcement algorithms with soft bio-inspired structures positively directs to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment. For soft robotic structures possessing countless degrees of freedom, it is at times not convenient to formulate mathematical models necessary for training a deep reinforcement learning (DRL) agent. Deploying current imitation learning algorithms on soft robotic systems has provided competent results. This review article posits an overview of various such algorithms along with instances of being applied to real-world scenarios, yielding frontier results. Brief descriptions highlight the various pristine branches of DRL research in soft robotics. View Full-Text
Keywords: deep reinforcement learning; imitation learning; soft robotics deep reinforcement learning; imitation learning; soft robotics
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Bhagat, S.; Banerjee, H.; Ho Tse, Z.T.; Ren, H. Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges. Robotics 2019, 8, 4.

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