Preprint Review Version 2 This version is not peer-reviewed

Deep Reinforcement Learning for Soft Robotic Applications: Brief Overview with Impending Challenges

Version 1 : Received: 18 November 2018 / Approved: 20 November 2018 / Online: 20 November 2018 (16:40:18 CET)
Version 2 : Received: 21 November 2018 / Approved: 23 November 2018 / Online: 23 November 2018 (11:57:55 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Robotics 2019, 8, 4
DOI: 10.3390/robotics8010004

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 sprouting of a relatively new yet extremely rewarding sphere of technology. The fusion of current deep reinforcement algorithms with physical advantages of a soft bio-inspired structure certainly directs us to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment to achieve a task they have been assigned. For soft robotics structure possessing countless degrees of freedom, it is often not easy (something not even possible) to formulate mathematical constraints necessary for training a deep reinforcement learning (DRL) agent for the task in hand, hence, we resolve to imitation learning techniques due to ease of manually performing such tasks like manipulation that could be comfortably mimicked by our agent. Deploying current imitation learning algorithms on soft robotic systems have been observed to provide satisfactory results but there are still challenges in doing so. This review article thus posits an overview of various such algorithms along with instances of them being applied to real world scenarios and yielding state-of-the-art results followed by brief descriptions on various pristine branches of DRL research that may be centers of future research in this field of interest.

Supplementary and Associated Material

https://www.preprints.org/manuscript/201811.0510/v1: Deep Reinforcement Learning for Soft Robotic Applications: Brief Overview with Impending Challenges

Subject Areas

deep reinforcement learning; imitation learning; soft robotics

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