Editorial: Robotics to Understand Animal Behaviour
Liang Li
Sridhar Ravi
Chen Wang- 1Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
- 2Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- 3Department of Biology, University of Konstanz, Konstanz, Germany
- 4School of Engineering and Information Technology, University of New South Wales, Canberra, NSW, Australia
- 5The National Engineering Research Center of Software Engineering, Peking University, Beijing, China
- 6The State Key Laboratory of Turbulence and Complex Systems, Intelligent Biomimetic Design Lab, College of Engineering, Peking University, Beijing, China
Editorial on the Research Topic
Robotics to Understand Animal Behaivor
For centuries, designers and engineers have turned to biological systems to seek inspiration for constructing functional, flexible, and robust robots (Bar-Cohen, 2005), which has matured into a significant branch of robotics. Recently, biologists and interdisciplinary teams are using robots as viable testbeds to help explore and evaluate hypotheses in animal behaviour (Webb, 2000; Marras and Porfiri, 2012; Gravish and Lauder, 2018; Landgraf et al., 2021). This strategy offers a suitable alternative to traditional biological methods, which have severe limitations including the feasibility to minimize confounding influences and isolate effects on living specimens, the effects of the experiment and the experimenter on the study animal, and other limitations imposed by the behaviour of the animals. For example, a fish-like robot can greatly help biologists collect in-situ data on fish schooling behaviour with limited interferences from humans or other factors (Katzschmann et al., 2018), explore potential mechanisms of hydrodynamic benefits among individuals (Li et al., 2020), and test hypotheses in collective motion by including the robot in the loop of collective animal behaviour (Bonnet et al., 2018). The goal of the research topic is to showcase those interdisciplinary studies that reverse the idea of bio-inspired robots by applying engineering methods (theory, simulation, or experiment) to study animal behaviour.
After the peer review process, this topic accepted 5 articles, including 4 research articles and 1 perspective article. Schwab et al. built a biorobotic fish as an experimental model and systematically explored subcarangiform fish swimming behavior. Equipped with gallium-indium (eGaIn) sensors the robot could sense proprioceptive signals like a live system. Detailed flow fields collected by PIV (Particle image velocimetry) were further explored with nascent analytical techniques such as DMD (Dynamic mode decomposition) thus overcoming the significant challenges of performing similar experiments on real fish. In another study, Li et al. applied CFD (computational fluid dynamics) to study how fish decode information about their neighbor’s swimming via near flow fields. They simulated the detailed flow fields around schooling fish and found FFT (Fast flourier transform) was able to extract the relative position, phase differences, and the tail-beat frequency of its neighbor, thus posing a novel hypothesis about the potential sources of information to real fish. Stefanec et al. constructed a small group of robots to interact with the queen bee in a colony. They justified the effectiveness of these robots via “queen court events” (QCE)—where the queen is surrounded by the court worker bees while resting. Olejnik et al. applied both CFD and an experimental micro-air-vehicle platform to understand how insects achieve incredible agility and cope with wind unsteadiness by utilizing passive and/or active flight control strategies. Through experiments and simulation, they found flying flies are partially passive stable in lateral winds due to their asymmetry wing-beat Horsevad et al. made an argument that as a complement to the simulation studies, experiments with robots can be a powerful tool to understand collective behavior.
Taken together, the studies presented in this special issue highlight the benefits robotic platforms offer interdisciplinary teams seeking insights into animal behavior and hopefully further inspire the cross-pollination of knowledge and expertise across organismal/behavioral biology and robotics.
Author Contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Acknowledgments
LL acknowledges the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy--EXC 2117-422037984, the Max Planck Society, and great support from the Couzin Lab. CW acknowledges the National Natural Science Foundation of China under Grants 61973007.
References
Bar-Cohen, Y. (2005). Biomimetics: Biologically Inspired Technologies. Florida, United States: CRC Press.
Bonnet, F., Gribovskiy, A., Halloy, J., and Mondada, F. (2018). Closed-loop Interactions between a Shoal of Zebrafish and a Group of Robotic Fish in a Circular Corridor. Swarm Intell. 12 (3), 227–244. doi:10.1007/s11721-017-0153-6
Gravish, N., and Lauder, G. V. (2018). Robotics-inspired Biology. J. Exp. Biol. 221 (7), jeb138438. doi:10.1242/jeb.138438
Katzschmann, R. K., DelPreto, J., MacCurdy, R., and Rus, D. (2018). Exploration of Underwater Life with an Acoustically Controlled Soft Robotic Fish. Sci. Robot. 3 (16), eaar3449. doi:10.1126/scirobotics.aar3449
Landgraf, T., Gebhardt, G. H. W., Bierbach, D., Romanczuk, P., Musiolek, L., Hafner, V. V., et al. (2021). Animal-in-the-Loop: Using Interactive Robotic Conspecifics to Study Social Behavior in Animal Groups. Annu. Rev. Control, Robotics, Aut. Syst. 4 (1), 487–507. doi:10.1146/annurev-control-061920-103228
Li, L., Nagy, M., Graving, J. M., Bak-Coleman, J., Xie, G., and Couzin, I. D. (2020). Vortex Phase Matching as a Strategy for Schooling in Robots and in Fish. Nat. Commun. 11 (1), 5408. doi:10.1038/s41467-020-19086-0
Marras, S., and Porfiri, M. (2012). Fish and Robots Swimming Together: Attraction towards the Robot Demands Biomimetic Locomotion. J. R. Soc. Interface. 9 (73), 1856–1868. doi:10.1098/rsif.2012.0084
Keywords: bioinspired robot, animal behaivor, collective behaivor, hydrodynamic interaction, robots in biology
Citation: Li L, Ravi S and Wang C (2022) Editorial: Robotics to Understand Animal Behaviour. Front. Robot. AI 9:963416. doi: 10.3389/frobt.2022.963416
Received: 07 June 2022; Accepted: 24 June 2022;
Published: 11 July 2022.
Edited and Reviewed by:
Alastair George Stewart, The University of Melbourne, AustraliaCopyright © 2022 Li, Ravi and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Liang Li, lli@ab.mpg.de