A Preface in Electromagnetic Robotic Actuation and Sensing in Medicine

Ren, Hongliang; Banerjee, Hritwick

doi:10.1007/978-981-10-6035-9_1

Cited by 12 publications

(10 citation statements)

References 32 publications

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“…Biological organisms use their soft structure to good advantage to maneuver in complex environments, hence giving the motivation to exploit such physical attributes that could be incorporated to perform tasks that demand robust interactions with uncertain environments [6]. SoRo with three-dimensional bio-inspired structures [7] are capable of self-regulated homeostasis, resulting in robotics actuators that have the potential to mimic biomimetic motions with inexpensive actuation [5,[8][9][10]. These developments enabling such robotic hardware present an opportunity to couple with imitation learning techniques by exploiting the special properties of these materials to clinch precision and accuracy.…”

Section: Soft Robotics: a New Surge In Roboticsmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Soft Robotics: a New Surge In Roboticsmentioning

confidence: 99%

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

et al. 2019

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“…Magneto-driven actuators are actuators that produce actuation and motion driven through magnetic fields [108,109]. To obtain an idea about the workings of magnetic actuators, there is a study on a hydrogel-based microgripper that was designed and can be moved freely in liquids when DC magnetic fields are applied and that performs gripping when alternating current (AC) fields are applied [110].…”

Section: Hydrogel-based Soft Actuatorsmentioning

confidence: 99%

Hydrogel Actuators and Sensors for Biomedical Soft Robots: Brief Overview with Impending Challenges

2018

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There are numerous developments taking place in the field of biorobotics, and one such recent breakthrough is the implementation of soft robots-a pathway to mimic nature's organic parts for research purposes and in minimally invasive surgeries as a result of their shape-morphing and adaptable features. Hydrogels (biocompatible, biodegradable materials that are used in designing soft robots and sensor integration), have come into demand because of their beneficial properties, such as high water content, flexibility, and multi-faceted advantages particularly in targeted drug delivery, surgery and biorobotics. We illustrate in this review article the different types of biomedical sensors and actuators for which a hydrogel acts as an active primary material, and we elucidate their limitations and the future scope of this material in the nexus of similar biomedical avenues.

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“…SoRo has come to our aid by enhancing physical potentialities of robotic structures amplifying the flexibility, rigidity and the strength and hence, accelerating their performance. SoRo is capable of creating three dimensional bio-inspired structures [6] that are capable of self-regulated homeostasis, resulting in robotics actuators that have the potential to mimic biomimetic motions with simple, inexpensive actuation [7,8] and able to achieve bending, twisting, extension, flexion with non-rigid materials [5,9]. These developments in creating such robotic hardware presents before us an opportunity to couple them with imitation learning techniques by exploiting the special properties of these materials to clich much higher levels of precision and accuracy.…”

Section: Soft Robotics: a New Surge In Roboticsmentioning

confidence: 99%

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

Bhagat¹,

Banerjee²,

Ren³

2018

Preprint

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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.

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A Preface in Electromagnetic Robotic Actuation and Sensing in Medicine

Cited by 12 publications

References 32 publications

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

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

Hydrogel Actuators and Sensors for Biomedical Soft Robots: Brief Overview with Impending Challenges

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

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