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Applied Sciences
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From Fundamental Research to Application of Bio-Inspired, Bio-Hybrid, and Soft...
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From Fundamental Research to Application of Bio-Inspired, Bio-Hybrid, and Soft Robotics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 1251

Special Issue Editors

Dr. Shunsuke Shigaki

E-Mail Website
Guest Editor
Principles of Informatics Research Division, National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
Interests: bio-inspired robotics; control engineering; navigation algorithms; robotics olfaction
Prof. Dr. Daisuke Kurabayashi

E-Mail Website
Guest Editor
Department of Systems and Control Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan
Interests: bio-inspired robotics; distributed autonomous systems; motion planning algorithms

Special Issue Information

Dear Colleagues,

Organisms have an excellent balance of intelligence and body, and are excellent in adaptability and robustness. Robotic systems that can behave like living organisms have been developed for many years. Among these robotic research, it has focused on mimicking the body structure of living organisms, modeling the intelligence of living organisms, and using biological materials as robotic components. In recent years, attention has been paid to the field of soft robotics, in which the robotic body is composed of soft materials. The biological experimental techniques and system design theories that support these fields are important for the next generation of robotics and should be recognized by researchers in a wide range of fields. The purpose of this Special Issue is to introduce the latest research in the areas of bio-inspired robotics, bio-hybrid robotics, and soft robotics. Because these areas are at the interface between science and engineering, we welcome submissions on a wide range of topics such as neuroethology, cyborg technology, and soft sensors.

Dr. Shunsuke Shigaki
Prof. Dr. Daisuke Kurabayashi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bio-inspired/bio-hybrid/soft robotics
  • behavioral algorithms
  • cyborg
  • embedded intelligence
  • ethology/neuroethology
  • soft materials

Published Papers (2 papers)

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Review

26 pages, 611 KiB  
Review
Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review
by Yuchen Wang, Mitsuhiro Hayashibe and Dai Owaki
Appl. Sci. 2024, 14(10), 4038; https://doi.org/10.3390/app14104038 - 9 May 2024
Viewed by 185
Abstract
Policy learning enables agents to learn how to map states to actions, thus enabling adaptive and flexible behavioral generation in complex environments. Policy learning methods are fundamental to reinforcement learning techniques. However, as problem complexity and the requirement for motion flexibility increase, traditional [...] Read more.
Policy learning enables agents to learn how to map states to actions, thus enabling adaptive and flexible behavioral generation in complex environments. Policy learning methods are fundamental to reinforcement learning techniques. However, as problem complexity and the requirement for motion flexibility increase, traditional methods that rely on manual design have revealed their limitations. Conversely, data-driven policy learning focuses on extracting strategies from biological behavioral data and aims to replicate these behaviors in real-world environments. This approach enhances the adaptability of agents to dynamic substrates. Furthermore, this approach has been extensively applied in autonomous driving, robot control, and interpretation of biological behavior. In this review, we survey developments in data-driven policy-learning algorithms over the past decade. We categorized them into the following three types according to the purpose of the method: (1) imitation learning (IL), (2) inverse reinforcement learning (IRL), and (3) causal policy learning (CPL). We describe the classification principles, methodologies, progress, and applications of each category in detail. In addition, we discuss the distinct features and practical applications of these methods. Finally, we explore the challenges these methods face and prospective directions for future research. Full article
(This article belongs to the Special Issue From Fundamental Research to Application of Bio-Inspired, Bio-Hybrid, and Soft Robotics)
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14 pages, 1534 KiB  
Review
Usage of a Sensory-Motor Intervention System for Understanding the Adaptive Behavior of Insects
by Shunsuke Shigaki and Noriyasu Ando
Appl. Sci. 2024, 14(3), 1139; https://doi.org/10.3390/app14031139 - 29 Jan 2024
Viewed by 739
Abstract
Despite their diminutive neural systems, insects exhibit sophisticated adaptive behaviors in diverse environments. An insect receives various environmental stimuli through its sensory organs and selectively and rapidly integrates them to produce an adaptive motor output. Living organisms commonly have this sensory-motor integration, and [...] Read more.
Despite their diminutive neural systems, insects exhibit sophisticated adaptive behaviors in diverse environments. An insect receives various environmental stimuli through its sensory organs and selectively and rapidly integrates them to produce an adaptive motor output. Living organisms commonly have this sensory-motor integration, and attempts have been made for many years to elucidate this mechanism biologically and reconstruct it through engineering. In this review, we provide an overview of the biological analyses of the adaptive capacity of insects and introduce a framework of engineering tools to intervene in insect sensory and behavioral processes. The manifestation of adaptive insect behavior is intricately linked to dynamic environmental interactions, underscoring the significance of experiments maintaining this relationship. An experimental setup incorporating engineering techniques can manipulate the sensory stimuli and motor output of insects while maintaining this relationship. It can contribute to obtaining data that could not be obtained in experiments conducted under controlled environments. Moreover, it may be possible to analyze an insect’s adaptive capacity limits by varying the degree of sensory and motor intervention. Currently, experimental setups based on the framework of engineering tools only measure behavior; therefore, it is not possible to investigate how sensory stimuli are processed in the central nervous system. The anticipated future developments, including the integration of calcium imaging and electrophysiology, hold promise for a more profound understanding of the adaptive prowess of insects. Full article
(This article belongs to the Special Issue From Fundamental Research to Application of Bio-Inspired, Bio-Hybrid, and Soft Robotics)
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