Robots are traditionally bound by a fixed morphology during their operational lifetime, which is limited to adapting only their control strategies. Here we present the first quadrupedal robot that can morphologically adapt to different environmental conditions in outdoor, unstructured environments. Our solution is rooted in embodied AI and comprises two components: (1) a robot that permits in situ morphological adaptation and (2) an adaptation algorithm that transitions between the most energy-efficient morphologies on the basis of the currently sensed terrain. We first build a model that describes how the robot morphology affects performance on selected terrains. We then test continuous adaptation on realistic outdoor terrain while allowing the robot to constantly update its model. We show that the robot exploits its training to effectively transition between different morphological configurations, exhibiting substantial performance improvements over a non-adaptive approach. The demonstrated benefits of real-world morphological adaptation demonstrate the potential for a new embodied way of incorporating adaptation into future robotic designs.
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
All JSON-formatted measurements from robot evaluations in this study have been deposited in Figshare57. All other relevant data are available from the corresponding author on request.
Kawatsuma, S., Fukushima, M. & Okada, T.Emergency response by robots to Fukushima–Daiichi accident: summary and lessons learned. Industrial Robot 39, 428–435 (2012).
Baines, R., Freeman, S., Fish, F. & Kramer, R. Variable stiffness morphing limb for amphibious legged robots inspired by chelonian environmental adaptations. Bioinspir. Biomim. 15, 025002 (2020).
Paik, J. K., Byoungkwon, A., Rus, D. & Wood, R. J. Robotic origamis: self-morphing modular robot. In Proc. 2nd International Conference on Morphological Computation (EPFL, 2012).
Pfeifer, R. & Bongard, J. How the Body Shapes the Way We Think: A New View of Intelligence (MIT Press, 2006).
Wilson, A. D. & Golonka, S. Embodied cognition is not what you think it is. Frontiers Psychol. 4, 58 (2013).
Zhang, T., Zhang, W. & Gupta, M. M. Resilient robots: concept, review, and future directions. Robotics 6, 22 (2017).
Picardi, G., Hauser, H., Laschi, C. & Calisti, M. Morphologically induced stability on an underwater legged robot with a deformable body. Int. J. Robot. Res. https://doi.org/10.1177/0278364919840426 (2019).
Nygaard, T. F., Martin, C. P., Samuelsen, E., Torresen, J. & Glette, K. Real-world evolution adapts robot morphology and control to hardware limitations. In Proc. Genetic and Evolutionary Computation Conference (ACM, 2018).
Nygaard, T. F., Martin, C. P., Howard, D., Torresen, J. & Glette, K. Environmental adaptation of robot morphology and control through real-world evolution. Preprint at http://arxiv.org/abs/2003.13254 (2020).
Heijnen, H., Howard, D. & Kottege, N. A testbed that evolves hexapod controllers in hardware. In 2017 IEEE International Conference on Robotics and Automation 1065–1071 (IEEE, 2017).
Gong, D., Yan, J. & Zuo, G. A review of gait optimization based on evolutionary computation. Appl. Comput. Intell. Soft Comput. https://doi.org/10.1155/2010/413179 (2010).
Ha, S., Xu, P., Tan, Z., Levine, S. & Tan, J. Learning to walk in the real world with minimal human effort. Preprint at http://arxiv.org/abs/2002.08550 (2020).
Kober, J., Bagnell, J. A. & Peters, J. Reinforcement learning in robotics: a survey. Int. J. Robot. Res. 32, 1238–1274 (2013).
Calandra, R., Seyfarth, A., Peters, J. & Deisenroth, M. P. Bayesian optimization for learning gaits under uncertainty. Ann. Math. AI 76, 5–23 (2016).
Rodriguez, D., Brandenburger, A. & Behnke, S. Combining simulations and real-robot experiments for Bayesian optimization of bipedal gait stabilization. In RoboCup 2018: Robot World Cup XXII 70–82 (Springer, 2018).
Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V. & Hutter, M. Learning quadrupedal locomotion over challenging terrain. Sci. Robot. 5, eabc5986 (2020).
Hwangbo, J. et al. Learning agile and dynamic motor skills for legged robots. Sci. Robot. 4, eaau5872 (2019).
Kalakrishnan, M., Buchli, J., Pastor, P., Mistry, M. & Schaal, S. Learning, planning, and control for quadruped locomotion over challenging terrain. Int. J. Robot. Res. 30, 236–258 (2011).
Kaushik, R., Anne, T. & Mouret, J.-B. Fast online adaptation in robotics through meta-learning embeddings of simulated priors. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems 5269–5276 (IEEE, 2020).
Fahmi, S. et al. Stance: locomotion adaptation over soft terrain. IEEE Trans. Robot. 36, 443–457 (2020).
Buchanan, R. et al. Walking posture adaptation for legged robot navigation in confined spaces. IEEE Robot. Autom. Lett. 4, 2148–2155 (2019).
Long, J. Darwin’s Devices: What Evolving Robots can Teach Us about the History of Life and the Future of Technology (Basic Books, 2012).
Eiben, A. E., Kernbach, S. & Haasdijk, E. Embodied artificial evolution: artificial evolutionary systems in the 21st century. Evol. Intell. 5, 261–272 (2012).
Mouret, J.-B. & Chatzilygeroudis, K. 20 Years of reality gap: a few thoughts about simulators in evolutionary robotics. In Proc. Genetic and Evolutionary Computation Conference Companion 1121–1124 (Association for Computing Machinery, 2017).
Cheney, N., MacCurdy, R., Clune, J. & Lipson, H. Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding. In Proc. 15th Annual Conference on Genetic and Evolutionary Computation 167–174 (Association for Computing Machinery, 2013).
Marbach, D. & Ijspeert, A. J. Online optimization of modular robot locomotion. In IEEE International Conference Mechatronics and Automation Vol. 1, 248–253 (IEEE, 2005).
Passault, G., Rouxel, Q., Fabre, R., N’Guyen, S. & Ly, O. Optimizing morphology and locomotion on a corpus of parametric legged robots. In Conference on Biomimetic and Biohybrid Systems 227–238 (Springer, 2016).
Spielberg, A. et al. Learning-in-the-loop optimization: end-to-end control and co-design of soft robots through learned deep latent representations. In Advances in Neural Information Processing Systems 8282–8292 (NeurIPS, 2019).
Lipson, H. & Pollack, J. B. Automatic design and manufacture of robotic lifeforms. Nature 406, 974–978 (2000).
Ha, S., Coros, S., Alspach, A., Kim, J. & Yamane, K. Joint optimization of robot design and motion parameters using the implicit function theorem. In Robotics: Science and Systems (Carnegie Mellon Univ., 2017).
Collins, J., Geles, W., Howard, D. & Maire, F. Towards the targeted environment-specific evolution of robot components. In Proc. Genetic and Evolutionary Computation Conference 61–68 (2018).
Hornby, G. S., Lipson, H. & Pollack, J. B. Generative representations for the automated design of modular physical robots. IEEE Trans. Robot. Autom. 19, 703–719 (2003).
Auerbach, J. et al. Robogen: robot generation through artificial evolution. In Artificial Life Conference Proceedings 136–137 (MIT Press, 2014).
Kriegman, S. et al. Scalable sim-to-real transfer of soft robot designs. In 2020 3rd IEEE International Conference on Soft Robotics 359–366 (IEEE, 2020).
Jakobi, N., Husbands, P. & Harvey, I. Noise and the reality gap: the use of simulation in evolutionary robotics. In European Conference on Artificial Life 704–720 (Springer, 1995).
Erez, T., Tassa, Y. & Todorov, E. Simulation tools for model-based robotics: comparison of Bullet, Havok, MuJoCo, ODE and PhysX. In 2015 IEEE International Conference on Robotics and Automation 4397–4404 (IEEE, 2015).
Sun, Y., Chen, X., Yan, T. & Jia, W. Modules design of a reconfigurable multi-legged walking robot. In 2006 IEEE International Conference on Robotics and Biomimetics 1444–1449 (IEEE, 2006).
Guan, Y., Jiang, L., Zhangy, X., Zhang, H. & Zhou, X. Development of novel robots with modular methodology. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems 2385–2390 (IEEE, 2009).
Jelisavcic, M. et al. Real-world evolution of robot morphologies: a proof of concept. Artif. Life. 23, 206–235 (2017).
Brodbeck, L., Hauser, S. & Iida, F. Morphological evolution of physical robots through model-free phenotype development. PLoS One 10, 1–17 (2015).
Vujovik, V., Rosendo, A., Brodbeck, L. & Iida, F.Evolutionary developmental robotics: improving morphology and control of physical robots. Artif. Life 23, 169–185 (2017).
Moreno, R. et al. Automated reconfiguration of modular robots using robot manipulators. In 2018 IEEE Symposium Series on Computational Intelligence 884–891 (IEEE, 2018).
Nygaard, T. F. et al. Experiences from real-world evolution with DyRET: dynamic robot for embodied testing. In Symposium of the Norwegian AI Society 58–68 (Springer, 2019).
Zhakypov, Z. & Paik, J. Design methodology for constructing multimaterial origami robots and machines. IEEE Trans. Robot. 34, 151–165 (2018).
Riviere, V., Manecy, A. & Viollet, S. Agile robotic fliers: a morphing-based approach. Soft Robot. 5, 541–553 (2018).
Bucki, N. & Mueller, M. W. Design and control of a passively morphing quadcopter. In 2019 International Conference on Robotics and Automation 9116–9122 (IEEE, 2019).
Geilinger, M., Poranne, R., Desai, R., Thomaszewski, B. & Coros, S. Skaterbots: optimization-based design and motion synthesis for robotic creatures with legs and wheels. ACM Trans. Graphics 37, 1–12 (2018).
Meiri, N. & Zarrouk, D. Flying STAR, a hybrid crawling and flying sprawl tuned robot. In 2019 International Conference on Robotics and Automation 5302–5308 (IEEE, 2019).
Kriegman, S., Blackiston, D., Levin, M. & Bongard, J. A scalable pipeline for designing reconfigurable organisms. Proc. Natl Acad. Sci. USA 117, 1853–1859 (2020).
Ritter, A. Shape-Changing Smart Materials 46–71 (Birkhäuser, 2007).
Nygaard, T. F. & Nordmoen, J. DyRET Documentation (GitHub, 2021); https://github.com/dyret-robot/dyret_documentation
Nygaard, T. F., Martin, C. P., Torresen, J. & Glette, K. Self-modifying morphology experiments with DyRET: dynamic robot for embodied testing. In 2019 IEEE International Conference on Robotics and Automation (IEEE, 2019).
Seok, S. et al. Design principles for energy-efficient legged locomotion and implementation on the MIT cheetah robot. IEEE/ASME Trans. Mechatronics 20, 1117–1129 (2014).
Xi, W., Yesilevskiy, Y. & Remy, C. D. Selecting gaits for economical locomotion of legged robots. Int. J. Robot. Res. 35, 1140–1154 (2016).
Howard, A. & Seraji, H. Vision-based terrain characterization and traversability assessment. J. Robot. Syst. 18, 577–587 (2001).
Nygaard, T. F., Martin, C. P., Torresen, J. & Glette, K. in Applications of Evolutionary Computation (Springer, 2019).
Nygaard, T. F. Dataset Hosted on Figshare (Figshare, 2021); https://doi.org/10.6084/m9.figshare.12661619
Nygaard, T. F. tonnesfn_experiments (GitHub, 2021); https://github.com/tonnesfn/tonnesfn_experiments
Allen, L., O’Connell, A. & Kiermer, V. How can we ensure visibility and diversity in research contributions? How the contributor role taxonomy (CRediT) is helping the shift from authorship to contributorship. Learned Publishing 32, 71–74 (2019).
This work was partially supported by The Research Council of Norway under grant agreement no. 240862 and its Centres of Excellence scheme, project no. 262762.
The authors declare no competing interests.
Peer review information Nature Machine Intelligence thanks Agoston Eiben, and the other anonymous reviewer(s), for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 A diagram of the prediction model used, instantiated with the baseline data set measurements from the indoor boxes.
a, The full model with the 25 sub-models for each leg length combination. b, The sub-model for femur 50mm tibia 40mm which shows the predicted energy efficiency (COT) of that leg-length combination for different terrains. The 15 points are the actual measurements from the indoor data collection (square from concrete, circle from sand, and diamond from gravel). When the robot encounters a new terrain, each sub-model is queried at the given roughness/hardness values to form a predicted 5 × 5 COT map similar to the ones shown in Figure 2 in the main text.
a, The components of the mechanical adaptation mechanism. b, An indication of the difference in workspace for the two extreme leg lengths.
Kernel density estimate of the terrain features for the three different surfaces in the indoor terrain boxes, measured during the data set collection. This includes all the different morphologies. Default seaborn.jointplot parameters are used for the estimate.
Terrain characteristics of the three surfaces in the indoor terrain boxes, shown for different walking speeds. The solid lines show the mean, and the shaded areas show the standard deviation. a, Perceived roughness from the depth camera in the front of the robot. b, Perceived hardness from the force sensors in the feet.
Generated from single 16 second walking sessions with shortest possible leg length. Roughness to the left is calculated from the depth camera. The middle plot shows the absolute, filtered three axis force measurements used to infer hardness, summed for the two front leg sensors (x-axis, sideways, in blue; y-axis, lengthwise, in orange; and z-axis, up, in green). The right plot shows the total current reported by the servos.
About this article
Cite this article
Nygaard, T.F., Martin, C.P., Torresen, J. et al. Real-world embodied AI through a morphologically adaptive quadruped robot. Nat Mach Intell (2021). https://doi.org/10.1038/s42256-021-00320-3