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Arreguit J, Ramalingasetty ST, Ijspeert A. FARMS: Framework for Animal and Robot Modeling and Simulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.25.559130. [PMID: 38293071 PMCID: PMC10827226 DOI: 10.1101/2023.09.25.559130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The study of animal locomotion and neuromechanical control offers valuable insights for advancing research in neuroscience, biomechanics, and robotics. We have developed FARMS (Framework for Animal and Robot Modeling and Simulation), an open-source, interdisciplinary framework, designed to facilitate access to neuromechanical simulations for modeling, simulation, and analysis of animal locomotion and bio-inspired robotic systems. By providing an accessible and user-friendly platform, FARMS aims to lower the barriers for researchers to explore the complex interactions between the nervous system, musculoskeletal structures, and their environment. Integrating the MuJoCo physics engine in a modular manner, FARMS enables realistic simulations and fosters collaboration among neuroscientists, biologists, and roboticists. FARMS has already been extensively used to study locomotion in animals such as mice, drosophila, fish, salamanders, and centipedes, serving as a platform to investigate the role of central pattern generators and sensory feedback. This article provides an overview of the FARMS framework, discusses its interdisciplinary approach, showcases its versatility through specific case studies, and highlights its effectiveness in advancing our understanding of locomotion. In particular, we show how we used FARMS to study amphibious locomotion by presenting experimental demonstrations across morphologies and environments based on neural controllers with central pattern generators and sensory feedback circuits models. Overall, the goal of FARMS is to contribute to a deeper understanding of animal locomotion, the development of innovative bio-inspired robotic systems, and promote accessibility in neuromechanical research.
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Affiliation(s)
- Jonathan Arreguit
- BioRob, School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Shravan Tata Ramalingasetty
- BioRob, School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Neurobiology and Anatomy, College of Medicine, Drexel University, Philadelphia, USA
| | - Auke Ijspeert
- BioRob, School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Gordleeva SY, Kastalskiy IA, Tsybina YA, Ermolaeva AV, Hramov AE, Kazantsev VB. Control of movement of underwater swimmers: Animals, simulated animates and swimming robots. Phys Life Rev 2023; 47:211-244. [PMID: 38072505 DOI: 10.1016/j.plrev.2023.10.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 10/29/2023] [Indexed: 12/18/2023]
Abstract
The control of movement in living organisms represents a fundamental task that the brain has evolved to solve. One crucial aspect is how the nervous system organizes the transformation of sensory information into motor commands. These commands lead to muscle activation and subsequent animal movement, which can exhibit complex patterns. One example of such movement is locomotion, which involves the translation of the entire body through space. Central Pattern Generators (CPGs) are neuronal circuits that provide control signals for these movements. Compared to the intricate circuits found in the brain, CPGs can be simplified into networks of neurons that generate rhythmic activation, coordinating muscle movements. Since the 1990s, researchers have developed numerous models of locomotive circuits to simulate different types of animal movement, including walking, flying, and swimming. Initially, the primary goal of these studies was to construct biomimetic robots. However, it became apparent that simplified CPGs alone were not sufficient to replicate the diverse range of adaptive locomotive movements observed in living organisms. Factors such as sensory modulation, higher-level control, and cognitive components related to learning and memory needed to be considered. This necessitated the use of more complex, high-dimensional circuits, as well as novel materials and hardware, in both modeling and robotics. With advancements in high-power computing, artificial intelligence, big data processing, smart materials, and electronics, the possibility of designing a new generation of true bio-mimetic robots has emerged. These robots have the capability to imitate not only simple locomotion but also exhibit adaptive motor behavior and decision-making. This motivation serves as the foundation for the current review, which aims to analyze existing concepts and models of movement control systems. As an illustrative example, we focus on underwater movement and explore the fundamental biological concepts, as well as the mathematical and physical models that underlie locomotion and its various modulations.
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Affiliation(s)
- S Yu Gordleeva
- National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., Nizhny Novgorod, 603022, Russia; Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia; Moscow Institute of Physics and Technology, 9 Institutskiy Ln., Dolgoprudny, 141701, Moscow Region, Russia
| | - I A Kastalskiy
- National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., Nizhny Novgorod, 603022, Russia; Moscow Institute of Physics and Technology, 9 Institutskiy Ln., Dolgoprudny, 141701, Moscow Region, Russia.
| | - Yu A Tsybina
- National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., Nizhny Novgorod, 603022, Russia; I.M. Sechenov First Moscow State Medical University (Sechenov University), 2 Bol'shaya Pirogovskaya St., Moscow, 119435, Russia
| | - A V Ermolaeva
- National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., Nizhny Novgorod, 603022, Russia; I.M. Sechenov First Moscow State Medical University (Sechenov University), 2 Bol'shaya Pirogovskaya St., Moscow, 119435, Russia
| | - A E Hramov
- Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia; Saint Petersburg State University, 7-9 Universitetskaya Emb., Saint Petersburg, 199034, Russia
| | - V B Kazantsev
- National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., Nizhny Novgorod, 603022, Russia; Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia; Moscow Institute of Physics and Technology, 9 Institutskiy Ln., Dolgoprudny, 141701, Moscow Region, Russia
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Ijspeert AJ, Daley MA. Integration of feedforward and feedback control in the neuromechanics of vertebrate locomotion: a review of experimental, simulation and robotic studies. J Exp Biol 2023; 226:jeb245784. [PMID: 37565347 DOI: 10.1242/jeb.245784] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Animal locomotion is the result of complex and multi-layered interactions between the nervous system, the musculo-skeletal system and the environment. Decoding the underlying mechanisms requires an integrative approach. Comparative experimental biology has allowed researchers to study the underlying components and some of their interactions across diverse animals. These studies have shown that locomotor neural circuits are distributed in the spinal cord, the midbrain and higher brain regions in vertebrates. The spinal cord plays a key role in locomotor control because it contains central pattern generators (CPGs) - systems of coupled neuronal oscillators that provide coordinated rhythmic control of muscle activation that can be viewed as feedforward controllers - and multiple reflex loops that provide feedback mechanisms. These circuits are activated and modulated by descending pathways from the brain. The relative contributions of CPGs, feedback loops and descending modulation, and how these vary between species and locomotor conditions, remain poorly understood. Robots and neuromechanical simulations can complement experimental approaches by testing specific hypotheses and performing what-if scenarios. This Review will give an overview of key knowledge gained from comparative vertebrate experiments, and insights obtained from neuromechanical simulations and robotic approaches. We suggest that the roles of CPGs, feedback loops and descending modulation vary among animals depending on body size, intrinsic mechanical stability, time required to reach locomotor maturity and speed effects. We also hypothesize that distal joints rely more on feedback control compared with proximal joints. Finally, we highlight important opportunities to address fundamental biological questions through continued collaboration between experimentalists and engineers.
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Affiliation(s)
- Auke J Ijspeert
- BioRobotics Laboratory, EPFL - Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Monica A Daley
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697, USA
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Owaki D, Manoonpong P, Ayali A. Editorial: Biological and Robotic Inter-Limb Coordination. Front Robot AI 2022; 9:875493. [PMID: 35391940 PMCID: PMC8981463 DOI: 10.3389/frobt.2022.875493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 02/22/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Dai Owaki
- Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Poramate Manoonpong
- Embodied AI and Neurorobotics Lab., SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark
- Bio-inspired Robotics and Neural Engineering Lab., School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Amir Ayali
- School of Zoology, Faculty of Life Sciences, and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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