Please use this identifier to cite or link to this item: https://doi.org/10.3390/robotics8010004
Title: Deep reinforcement learning for soft, flexible robots: Brief review with impending challenges
Authors: Bhagat, S.
Banerjee, H. 
Tse, Z.T.H.
Ren, H. 
Keywords: Deep reinforcement learning
Imitation learning
Soft robotics
Issue Date: 2019
Publisher: MDPI AG
Citation: Bhagat, S., Banerjee, H., Tse, Z.T.H., Ren, H. (2019). Deep reinforcement learning for soft, flexible robots: Brief review with impending challenges. Robotics 8 (1) : 4. ScholarBank@NUS Repository. https://doi.org/10.3390/robotics8010004
Rights: Attribution 4.0 International
Abstract: 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. © 2019 by the authors.
Source Title: Robotics
URI: https://scholarbank.nus.edu.sg/handle/10635/209612
ISSN: 2218-6581
DOI: 10.3390/robotics8010004
Rights: Attribution 4.0 International
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