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      A brittle star-like robot capable of immediately adapting to unexpected physical damage

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          Abstract

          A major challenge in robotic design is enabling robots to immediately adapt to unexpected physical damage. However, conventional robots require considerable time (more than several tens of seconds) for adaptation because the process entails high computational costs. To overcome this problem, we focus on a brittle star—a primitive creature with expendable body parts. Brittle stars, most of which have five flexible arms, occasionally lose some of them and promptly coordinate the remaining arms to escape from predators. We adopted a synthetic approach to elucidate the essential mechanism underlying this resilient locomotion. Specifically, based on behavioural experiments involving brittle stars whose arms were amputated in various ways, we inferred the decentralized control mechanism that self-coordinates the arm motions by constructing a simple mathematical model. We implemented this mechanism in a brittle star-like robot and demonstrated that it adapts to unexpected physical damage within a few seconds by automatically coordinating its undamaged arms similar to brittle stars. Through the above-mentioned process, we found that physical interaction between arms plays an essential role for the resilient inter-arm coordination of brittle stars. This finding will help develop resilient robots that can work in inhospitable environments. Further, it provides insights into the essential mechanism of resilient coordinated motions characteristic of animal locomotion.

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          Self-organization, embodiment, and biologically inspired robotics.

          Robotics researchers increasingly agree that ideas from biology and self-organization can strongly benefit the design of autonomous robots. Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, indefinite richness, and limited availability of information. Industrial robots, in contrast, operate in highly controlled environments with no or very little uncertainty. Although many challenges remain, concepts from biologically inspired (bio-inspired) robotics will eventually enable researchers to engineer machines for the real world that possess at least some of the desirable properties of biological organisms, such as adaptivity, robustness, versatility, and agility.
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            Resilient machines through continuous self-modeling.

            Animals sustain the ability to operate after injury by creating qualitatively different compensatory behaviors. Although such robustness would be desirable in engineered systems, most machines fail in the face of unexpected damage. We describe a robot that can recover from such change autonomously, through continuous self-modeling. A four-legged machine uses actuation-sensation relationships to indirectly infer its own structure, and it then uses this self-model to generate forward locomotion. When a leg part is removed, it adapts the self-models, leading to the generation of alternative gaits. This concept may help develop more robust machines and shed light on self-modeling in animals.
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              Robots that can adapt like animals

              Robots have transformed many industries, most notably manufacturing, and have the power to deliver tremendous benefits to society, such as in search and rescue, disaster response, health care and transportation. They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets to deep oceans. A major obstacle to their widespread adoption in more complex environments outside factories is their fragility. Whereas animals can quickly adapt to injuries, current robots cannot 'think outside the box' to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage, but current techniques are slow even with small, constrained search spaces. Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robot's prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles that animals use to adapt to injury.
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                Author and article information

                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society Publishing
                2054-5703
                December 2017
                13 December 2017
                13 December 2017
                : 4
                : 12
                : 171200
                Affiliations
                [1 ]Research Institute of Electrical Communication, Tohoku University , 2-1-1 Katahira, Aoba-Ward, Sendai 980-8577, Japan
                [2 ]Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University , N12W7, Kita-Ward, Sapporo, Hokkaido 060-0812, Japan
                [3 ]Japan Science and Technology Agency CREST , 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
                [4 ]Department of Physiology, Graduate School of Medicine, Tohoku University , 2-1 Seiryo-machi, Aoba-Ward, Sendai 980-8575, Japan
                Author notes
                Author for correspondence: Takeshi Kano e-mail: tkano@ 123456riec.tohoku.ac.jp
                [†]

                Present address: Department of Neuroscience, Tohoku Medical and Pharmaceutical University, 4-4-1 Komatsushima, Aoba-Ward, Sendai 981-8558, Japan.

                Electronic supplementary material is available online at https://dx.doi.org/10.6084/m9.figshare.c.3940261.

                Author information
                http://orcid.org/0000-0002-2033-4695
                Article
                rsos171200
                10.1098/rsos.171200
                5750017
                29308250
                98d1dac4-d502-4b8f-a9c1-4e196fa1c4b2
                © 2017 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 22 August 2017
                : 10 November 2017
                Funding
                Funded by: a program for creation of interdisciplinary research at Frontier Research Institute for Interdisciplinary Sciences (FRIS), Tohoku University;
                Funded by: Ministry of Education, Culture, Sports, Science and Technology, http://dx.doi.org/10.13039/501100001700;
                Award ID: Grant-in-Aid for Scientific Research (A), 24246074
                Award ID: Grant-in-Aid for Scientific Research (B), 16KT0099
                Categories
                1003
                164
                1004
                29
                1008
                119
                Engineering
                Research Article
                Custom metadata
                December, 2017

                decentralized control,resilient robot,brittle star
                decentralized control, resilient robot, brittle star

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