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Papaspyros V, Theraulaz G, Sire C, Mondada F. Quantifying the biomimicry gap in biohybrid robot-fish pairs. BIOINSPIRATION & BIOMIMETICS 2024; 19:046020. [PMID: 38866031 DOI: 10.1088/1748-3190/ad577a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 06/12/2024] [Indexed: 06/14/2024]
Abstract
Biohybrid systems in which robotic lures interact with animals have become compelling tools for probing and identifying the mechanisms underlying collective animal behavior. One key challenge lies in the transfer of social interaction models from simulations to reality, using robotics to validate the modeling hypotheses. This challenge arises in bridging what we term the 'biomimicry gap', which is caused by imperfect robotic replicas, communication cues and physics constraints not incorporated in the simulations, that may elicit unrealistic behavioral responses in animals. In this work, we used a biomimetic lure of a rummy-nose tetra fish (Hemigrammus rhodostomus) and a neural network (NN) model for generating biomimetic social interactions. Through experiments with a biohybrid pair comprising a fish and the robotic lure, a pair of real fish, and simulations of pairs of fish, we demonstrate that our biohybrid system generates social interactions mirroring those of genuine fish pairs. Our analyses highlight that: 1) the lure and NN maintain minimal deviation in real-world interactions compared to simulations and fish-only experiments, 2) our NN controls the robot efficiently in real-time, and 3) a comprehensive validation is crucial to bridge the biomimicry gap, ensuring realistic biohybrid systems.
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Affiliation(s)
- Vaios Papaspyros
- Mobile Robotic Systems (MOBOTS) group, School of Computer Science, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Guy Theraulaz
- Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, CNRS, Université de Toulouse III-Paul Sabatier, 31062 Toulouse, France
| | - Clément Sire
- Laboratoire de Physique Théorique, CNRS, Université de Toulouse III-Paul Sabatier, 31062 Toulouse, France
| | - Francesco Mondada
- Mobile Robotic Systems (MOBOTS) group, School of Computer Science, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
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Papaspyros V, Escobedo R, Alahi A, Theraulaz G, Sire C, Mondada F. Predicting the long-term collective behaviour of fish pairs with deep learning. J R Soc Interface 2024; 21:20230630. [PMID: 38442859 PMCID: PMC10914514 DOI: 10.1098/rsif.2023.0630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/06/2024] [Indexed: 03/07/2024] Open
Abstract
Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social interactions in the fish species Hemigrammus rhodostomus. We compare the results of our deep learning approach with experiments and with the results of a state-of-the-art analytical model. To that end, we propose a systematic methodology to assess the faithfulness of a collective motion model, exploiting a set of stringent individual and collective spatio-temporal observables. We demonstrate that machine learning (ML) models of social interactions can directly compete with their analytical counterparts in reproducing subtle experimental observables. Moreover, this work emphasizes the need for consistent validation across different timescales, and identifies key design aspects that enable our deep learning approach to capture both short- and long-term dynamics. We also show that our approach can be extended to larger groups without any retraining, and to other fish species, while retaining the same architecture of the deep learning network. Finally, we discuss the added value of ML in the context of the study of collective motion in animal groups and its potential as a complementary approach to analytical models.
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Affiliation(s)
- Vaios Papaspyros
- Mobile Robotic Systems (Mobots) group, Institute of Electrical and Micro Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Ramón Escobedo
- Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, CNRS, Université de Toulouse III – Paul Sabatier, 31062 Toulouse, France
| | - Alexandre Alahi
- VITA group, Civil Engineering Institute, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Guy Theraulaz
- Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, CNRS, Université de Toulouse III – Paul Sabatier, 31062 Toulouse, France
| | - Clément Sire
- Laboratoire de Physique Théorique, CNRS, Université de Toulouse III – Paul Sabatier, 31062 Toulouse, France
| | - Francesco Mondada
- Mobile Robotic Systems (Mobots) group, Institute of Electrical and Micro Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
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Zhou Z, Liu J, Pan J, Yu J. Proactivity of fish and leadership of self-propelled robotic fish during interaction. BIOINSPIRATION & BIOMIMETICS 2023; 18. [PMID: 37075759 DOI: 10.1088/1748-3190/acce87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 04/19/2023] [Indexed: 05/03/2023]
Abstract
Fish interacting with biomimetic robotic fish is beneficial for animal behavior research, particularly in the study of collective behavior. Compared with passive-dragging robotic fish, self-propelled robotic fish floats in water, and its movement matches the flow field formed by the caudal fin oscillation, leading to more realistic interaction with animals. In this paper, we propose a self-propelled koi-mimicking robotic fish entity, develop a system for robotic fish and koi fish interaction, and conduct extensive experiments on quantity variation and parameter variation. The results showed that fish exhibited significantly lower proactivity when alone, and the most proactive case is one robotic fish interacting with two real fish. The experiments on parameter variation indicated that fish may respond more proactivity to robotic fish that swim with high frequency and low amplitude, but may also move together with high-frequency and high-amplitude swimming robotic fish. These findings could provide insights into fish collective behavior, guide the design of further fish-robot interaction experiments, and suggest directions for future improvements in goal-oriented robotic fish platforms.
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Affiliation(s)
- Ziye Zhou
- State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, People's Republic of China
| | - Jincun Liu
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, People's Republic of China
| | - Jie Pan
- State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, People's Republic of China
| | - Junzhi Yu
- State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
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Schmickl T, Szopek M, Mondada F, Mills R, Stefanec M, Hofstadler DN, Lazic D, Barmak R, Bonnet F, Zahadat P. Social Integrating Robots Suggest Mitigation Strategies for Ecosystem Decay. Front Bioeng Biotechnol 2021; 9:612605. [PMID: 34109162 PMCID: PMC8181169 DOI: 10.3389/fbioe.2021.612605] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 03/11/2021] [Indexed: 12/02/2022] Open
Abstract
We develop here a novel hypothesis that may generate a general research framework of how autonomous robots may act as a future contingency to counteract the ongoing ecological mass extinction process. We showcase several research projects that have undertaken first steps to generate the required prerequisites for such a technology-based conservation biology approach. Our main idea is to stabilise and support broken ecosystems by introducing artificial members, robots, that are able to blend into the ecosystem's regulatory feedback loops and can modulate natural organisms' local densities through participation in those feedback loops. These robots are able to inject information that can be gathered using technology and to help the system in processing available information with technology. In order to understand the key principles of how these robots are capable of modulating the behaviour of large populations of living organisms based on interacting with just a few individuals, we develop novel mathematical models that focus on important behavioural feedback loops. These loops produce relevant group-level effects, allowing for robotic modulation of collective decision making in social organisms. A general understanding of such systems through mathematical models is necessary for designing future organism-interacting robots in an informed and structured way, which maximises the desired output from a minimum of intervention. Such models also help to unveil the commonalities and specificities of the individual implementations and allow predicting the outcomes of microscopic behavioural mechanisms on the ultimate macroscopic-level effects. We found that very similar models of interaction can be successfully used in multiple very different organism groups and behaviour types (honeybee aggregation, fish shoaling, and plant growth). Here we also report experimental data from biohybrid systems of robots and living organisms. Our mathematical models serve as building blocks for a deep understanding of these biohybrid systems. Only if the effects of autonomous robots onto the environment can be sufficiently well predicted can such robotic systems leave the safe space of the lab and can be applied in the wild to be able to unfold their ecosystem-stabilising potential.
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Affiliation(s)
- Thomas Schmickl
- Artificial Life Laboratory of the Institute of Biology, University of Graz, Graz, Austria
| | - Martina Szopek
- Artificial Life Laboratory of the Institute of Biology, University of Graz, Graz, Austria
| | - Francesco Mondada
- Mobile Robotic Systems Group, School of Engineering and School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Rob Mills
- Mobile Robotic Systems Group, School of Engineering and School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- BioISI, Faculdade de Ciências da Universidade de Lisboa, Lisbon, Portugal
| | - Martin Stefanec
- Artificial Life Laboratory of the Institute of Biology, University of Graz, Graz, Austria
| | - Daniel N. Hofstadler
- Artificial Life Laboratory of the Institute of Biology, University of Graz, Graz, Austria
| | - Dajana Lazic
- Artificial Life Laboratory of the Institute of Biology, University of Graz, Graz, Austria
| | - Rafael Barmak
- Mobile Robotic Systems Group, School of Engineering and School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Frank Bonnet
- Mobile Robotic Systems Group, School of Engineering and School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Payam Zahadat
- Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark
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Karakaya M, Macrì S, Porfiri M. Behavioral Teleporting of Individual Ethograms onto Inanimate Robots: Experiments on Social Interactions in Live Zebrafish. iScience 2020; 23:101418. [PMID: 32818837 PMCID: PMC7452384 DOI: 10.1016/j.isci.2020.101418] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 06/11/2020] [Accepted: 07/26/2020] [Indexed: 01/19/2023] Open
Abstract
Social behavior is widespread in the animal kingdom, and it remarkably influences human personal and professional lives. However, a thorough understanding of the mechanisms underlying social behavior is elusive. Integrating the seemingly different fields of robotics and preclinical research could bring new insight on social behavior. Toward this aim, we established "behavioral teleporting" as an experimental solution to independently manipulate multiple factors underpinning social interactions. Behavioral teleporting consists of real-time transfer of the complete ethogram of a live zebrafish onto a remotely-located robotic replica. Through parallel and simultaneous behavioral teleporting, we studied the interaction between two live fish swimming in remotely-located tanks: each live fish interacted with an inanimate robot that mirrored the behavior of the other fish, and the morphology of each robot was independently tailored. Our results indicate that behavioral teleporting can preserve natural interaction between two live animals, while allowing fine control over morphological features that modulate social behavior.
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Affiliation(s)
- Mert Karakaya
- Department of Mechanical and Aerospace Engineering, New York University, Tandon School of Engineering, 6 MetroTech Center, Brooklyn, NY 11201, USA
| | - Simone Macrì
- Department of Mechanical and Aerospace Engineering, New York University, Tandon School of Engineering, 6 MetroTech Center, Brooklyn, NY 11201, USA; Centre for Behavioural Sciences and Mental Health, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy
| | - Maurizio Porfiri
- Department of Mechanical and Aerospace Engineering, New York University, Tandon School of Engineering, 6 MetroTech Center, Brooklyn, NY 11201, USA; Department of Biomedical Engineering, New York University, Tandon School of Engineering, 6 MetroTech Center, Brooklyn NY 11201, USA.
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Macrì S, Karakaya M, Spinello C, Porfiri M. Zebrafish exhibit associative learning for an aversive robotic stimulus. Lab Anim (NY) 2020; 49:259-264. [PMID: 32778807 DOI: 10.1038/s41684-020-0599-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 06/18/2020] [Indexed: 12/21/2022]
Abstract
Zebrafish have quickly emerged as a species of choice in preclinical research, holding promise to advance the field of behavioral pharmacology through high-throughput experiments. Besides biological and heuristic considerations, zebrafish also constitute a fundamental tool that fosters the replacement of mammals with less sentient experimental subjects. Notwithstanding these features, experimental paradigms to investigate emotional and cognitive domains in zebrafish are still limited. Studies on emotional memories have provided sound methodologies to investigate fear conditioning in zebrafish, but these protocols may still benefit from a reconsideration of the independent variables adopted to elicit aversion. Here, we designed a fear-conditioning paradigm in which wild-type zebrafish were familiarized over six training sessions with an empty compartment and a fear-eliciting one. The fearful stimulus was represented by three zebrafish replicas exhibiting a fully synchronized and polarized motion as they were maneuvered along 3D trajectories by a robotic platform. When allowed to freely swim between the two compartments in the absence of the robotic stimulus (test session), zebrafish displayed a marked avoidance of the stimulus-paired one. To investigate whether fear conditioning was modulated by psychoactive compounds, two groups of zebrafish were administered ethanol (0.25% and 1.00%, ethanol/water, by volume) a few minutes before the test session. We observed that ethanol administration abolished the conditioned avoidance of the stimulus-paired compartment. Ultimately, this study confirms that robotic stimuli may be used in the design of fear-conditioning paradigms, which are sensitive to pharmacological manipulations.
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Affiliation(s)
- Simone Macrì
- Department of Mechanical and Aerospace Engineering, New York University, Tandon School of Engineering, Brooklyn, NY, USA.,Centre for Behavioural Sciences and Mental Health, Istituto Superiore di Sanità, Rome, Italy
| | - Mert Karakaya
- Department of Mechanical and Aerospace Engineering, New York University, Tandon School of Engineering, Brooklyn, NY, USA
| | - Chiara Spinello
- Department of Mechanical and Aerospace Engineering, New York University, Tandon School of Engineering, Brooklyn, NY, USA
| | - Maurizio Porfiri
- Department of Mechanical and Aerospace Engineering, New York University, Tandon School of Engineering, Brooklyn, NY, USA. .,Department of Biomedical Engineering, New York University, Tandon School of Engineering, Brooklyn, NY, USA.
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Escobedo R, Lecheval V, Papaspyros V, Bonnet F, Mondada F, Sire C, Theraulaz G. A data-driven method for reconstructing and modelling social interactions in moving animal groups. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190380. [PMID: 32713309 DOI: 10.1098/rstb.2019.0380] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Group-living organisms that collectively migrate range from cells and bacteria to human crowds, and include swarms of insects, schools of fish, and flocks of birds or ungulates. Unveiling the behavioural and cognitive mechanisms by which these groups coordinate their movements is a challenging task. These mechanisms take place at the individual scale and can be described as a combination of interactions between individuals and interactions between these individuals and the physical obstacles in the environment. Thanks to the development of novel tracking techniques that provide large and accurate datasets, the main characteristics of individual and collective behavioural patterns can be quantified with an unprecedented level of precision. However, in a large number of studies, social interactions are usually described by force map methods that only have a limited capacity of explanation and prediction, being rarely suitable for a direct implementation in a concise and explicit mathematical model. Here, we present a general method to extract the interactions between individuals that are involved in the coordination of collective movements in groups of organisms. We then apply this method to characterize social interactions in two species of shoaling fish, the rummy-nose tetra (Hemigrammus rhodostomus) and the zebrafish (Danio rerio), which both present a burst-and-coast motion. From the detailed quantitative description of individual-level interactions, it is thus possible to develop a quantitative model of the emergent dynamics observed at the group level, whose predictions can be checked against experimental results. This method can be applied to a wide range of biological and social systems. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.
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Affiliation(s)
- R Escobedo
- Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS) & Université de Toulouse - Paul Sabatier, 31062 Toulouse, France
| | - V Lecheval
- Department of Biology, University of York, York YO10 5DD, UK
| | - V Papaspyros
- MOBOTS group, Biorobotics laboratory, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - F Bonnet
- MOBOTS group, Biorobotics laboratory, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - F Mondada
- MOBOTS group, Biorobotics laboratory, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - C Sire
- Laboratoire de Physique Théorique, Centre National de la Recherche Scientifique (CNRS) & Université de Toulouse - Paul Sabatier, 31062 Toulouse, France
| | - G Theraulaz
- Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS) & Université de Toulouse - Paul Sabatier, 31062 Toulouse, France.,Centre for Ecological Sciences, Indian Institute of Science, Bengaluru, India
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Chemtob Y, Cazenille L, Bonnet F, Gribovskiy A, Mondada F, Halloy J. Strategies to modulate zebrafish collective dynamics with a closed-loop biomimetic robotic system. BIOINSPIRATION & BIOMIMETICS 2020; 15:046004. [PMID: 32252047 DOI: 10.1088/1748-3190/ab8706] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The objective of this study is to integrate biomimetic robots into small groups of zebrafish and to modulate their collective behaviours. A possible approach is to have the robots behave like sheepdogs. In this case, the robots would behave like a different species than the fish and would present different relevant behaviours. In this study, we explore different strategies that use biomimetic zebrafish behaviours. In past work, we have shown that robots biomimicking zebrafish can be socially integrated into zebrafish groups. We have also shown that a fish-like robot can modulate the rotation choice of zebrafish groups in a circular set-up. Here, we further study the modulation capabilities of such robots in a more complex set-up. To do this, we exploit zebrafish social behaviours we identified in previous studies. We first modulate collective departure by replicating the leadership mechanisms with the robot in a set-up composed of two rooms connected by a corridor. Then, we test different behavioural strategies to drive the fish groups towards a predefined target room. To drive the biohybrid groups towards a predefined choice, they have to adopt some specific fish-like behaviours. The first strategy is based on a single robot using the initiation behaviour. In this case, the robot keeps trying to initiate a group transition towards the target room. The second strategy is based on two robots, one initiating and one staying in the target room as a social attractant. The third strategy is based on a single robot behaving like a zebrafish but staying in the target room as a social attractant. The fourth strategy uses two robots behaving like zebrafish but staying in the target room. We conclude that robots can modulate zebrafish group behaviour by adopting strategies based on existing fish behaviours. Under these conditions, robots enable the testing of hypotheses about the behaviours of fish.
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Affiliation(s)
- Yohann Chemtob
- Univ Paris Diderot, Sorbonne Paris Cité, LIED, UMR 8236, 75013, Paris, France
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