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Editorial: Focus on methods: neural algorithms for bio-inspired robotics

Patanè, Luca ; Zhao, Guoping (2023)
Editorial: Focus on methods: neural algorithms for bio-inspired robotics.
In: Frontiers in Neurorobotics, 2023, 17
doi: 10.26083/tuprints-00024404
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Editorial: Focus on methods: neural algorithms for bio-inspired robotics
Language: English
Date: 8 August 2023
Place of Publication: Darmstadt
Year of primary publication: 2023
Publisher: Frontiers Media S.A.
Journal or Publication Title: Frontiers in Neurorobotics
Volume of the journal: 17
Collation: 3 Seiten
DOI: 10.26083/tuprints-00024404
Corresponding Links:
Origin: Secondary publication DeepGreen

Bio-inspired robotics aims to develop robotic systems that mimic or are inspired by biological systems and processes. One of the main challenges in this field is the development of neural algorithms that enable robots to exhibit intelligent behavior and interact effectively with their environment. Neural algorithms use the principles of neural computation and machine learning to solve complex problems and enable robots to perform tasks with greater efficiency, adaptability, and autonomy. In recent decades, research has gained deep insights into biological systems, from cell biology to physiology, biomechanics, and locomotion (Meyer and Guillot, 2008; Mazzolai et al., 2020; Valero-Cuevas and Erwin, 2022). As biological understanding advances, so does the field of robotics and bio-inspired robotics. The way animals interact with the world through object manipulation, locomotion and spatial navigation can be applied to the embodied intelligence of robots to enable better performance. Knowledge of the neural mechanisms underlying decision-making processes is constantly evolving as shown, for example, by interesting international projects mapping the nervous system of the fruit fly (Court et al., 2023; Naddaf, 2023). A recent review of insect-inspired robots shows the useful influence of biological systems at different levels of robot design, from the mechanical part to the locomotion control system to the sophisticated capabilities involving decision-making processes (Manoonpong et al., 2021). The influence of biological systems in the development of control architectures is enormously increasing and includes hardware-oriented solutions (Chen et al., 2019; Linares-Barranco et al., 2020). Robotics and neuroscience are closely related fields that aim to understand how autonomous agents can achieve agile, efficient, and robust locomotion. Robotics has already gained valuable insights from studying animals and applying neuromechanical principles. Similarly, neuroscience has benefited from the tools and ideas of robotics to explore how the body, physical interactions with the environment and sensory input influence animal behavior. This two-way exchange between the two disciplines was recently reviewed in Ramdya and Ijspeert (2023). This Research Topic aims to present the neural algorithm problem for bio-inspired robotics, focusing on the integration of neural algorithms and computation in robot bodies and the resulting interaction with the environment. Several notable applications in this area were considered and summarized here.

Uncontrolled Keywords: neural control, bio-robotics, biological signal processing, machine learning, intelligent machines
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-244041
Classification DDC: 500 Science and mathematics > 570 Life sciences, biology
600 Technology, medicine, applied sciences > 610 Medicine and health
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
700 Arts and recreation > 796 Sports
Divisions: 03 Department of Human Sciences > Institut für Sportwissenschaft > Sportbiomechanik
Zentrale Einrichtungen > Centre for Cognitive Science (CCS)
Date Deposited: 08 Aug 2023 12:10
Last Modified: 19 Oct 2023 13:36
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/24404
PPN: 51247219X
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