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Multi-environment robotic transitions through adaptive morphogenesis

Abstract

The current proliferation of mobile robots spans ecological monitoring, warehouse management and extreme environment exploration, to an individual consumer’s home1,2,3,4. This expanding frontier of applications requires robots to transit multiple environments, a substantial challenge that traditional robot design strategies have not effectively addressed5,6. For example, biomimetic design—copying an animal’s morphology, propulsion mechanism and gait—constitutes one approach, but it loses the benefits of engineered materials and mechanisms that can be exploited to surpass animal performance7,8. Other approaches add a unique propulsive mechanism for each environment to the same robot body, which can result in energy-inefficient designs9,10,11. Overall, predominant robot design strategies favour immutable structures and behaviours, resulting in systems incapable of specializing across environments12,13. Here, to achieve specialized multi-environment locomotion through terrestrial, aquatic and the in-between transition zones, we implemented ‘adaptive morphogenesis’, a design strategy in which adaptive robot morphology and behaviours are realized through unified structural and actuation systems. Taking inspiration from terrestrial and aquatic turtles, we built a robot that fuses traditional rigid components and soft materials to radically augment the shape of its limbs and shift its gaits for multi-environment locomotion. The interplay of gait, limb shape and the environmental medium revealed vital parameters that govern the robot’s cost of transport. The results attest that adaptive morphogenesis is a powerful method to enhance the efficiency of mobile robots encountering unstructured, changing environments.

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Fig. 1: Turtle-inspired amphibious robot.
Fig. 2: Swimming.
Fig. 3: Walking.
Fig. 4: Crawling on transition substrates.
Fig. 5: Transition policy and COT contextualization.

Data availability

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Information.

References

  1. Miki, T. et al. Learning robust perceptive locomotion for quadrupedal robots in the wild. Sci. Robot. 7, eabk2822 (2022).

    PubMed  Google Scholar 

  2. Sinatra, N. R. et al. Ultragentle manipulation of delicate structures using a soft robotic gripper. Sci. Robot. 4, eaax5425 (2019).

    PubMed  Google Scholar 

  3. D’Andrea, R. Guest Editorial: A revolution in the warehouse: a retrospective on Kiva Systems and the grand challenges ahead. IEEE Trans. Autom. Sci. Eng. 9, 638–639 (2012).

    Google Scholar 

  4. Forlizzi, J. & DiSalvo, C. Service robots in the domestic environment: a study of the Roomba vacuum in the home. In HRI ’06: Proc. 1st ACM SIGCHI/SIGART Conference on Human–Robot Interaction 258–265 (ACM, 2006).

  5. Shah, D. et al. Shape changing robots: bioinspiration, simulation, and physical realization. Adv. Mater. 33, 2002882 (2021).

    CAS  Google Scholar 

  6. Nygaard, T. F., Martin, C. P., Torresen, J., Glette, K. & Howard, D. Real-world embodied AI through a morphologically adaptive quadruped robot. Nat. Mach. Intell. 3, 410–419 (2021).

  7. Ijspeert, A. J., Crespi, A., Ryczko, D. & Cabelguen, J.-M. From swimming to walking with a salamander robot driven by a spinal cord model. Science 315, 1416–1420 (2007).

    ADS  CAS  PubMed  Google Scholar 

  8. Fish, F. E. Advantages of aquatic animals as models for bio-inspired drones over present AUV technology. Bioinspir. Biomim. 15, 025001 (2019).

  9. Yu, J. et al. On a bio-inspired amphibious robot capable of multimodal motion. IEEE/ASME Trans. Mechatron. 17, 847–856 (2012).

    Google Scholar 

  10. Yu, J., Ding, R., Yang, Q., Tan, M. & Zhang, J. Amphibious pattern design of a robotic fish with wheel-propeller-fin mechanisms: amphibious pattern design of a robotic fish. J. Field Robot. 30, 702–716 (2013).

    Google Scholar 

  11. Boxerbaum, A. S. et al. Design, simulation, fabrication and testing of a bio-inspired amphibious robot with multiple modes of mobility. J. Robot. Mechatronics 24, 629–641 (2012).

    Google Scholar 

  12. Lock, R. J., Burgess, S. C. & Vaidyanathan, R. Multi-modal locomotion: from animal to application. Bioinspir. Biomim. 9, 011001 (2013).

    ADS  PubMed  Google Scholar 

  13. Baines, R., Fish, F. & Kramer-Bottiglio, R. in Bioinspired Sensing, Actuation, and Control in Underwater Soft Robotic Systems (eds Paley, D. A. & Wereley, N. M.) Ch. 2 (Springer Nature, 2021).

  14. Dudek, G. et al. AQUA: an amphibious autonomous robot. Computer 40, 46–53 (2007).

    Google Scholar 

  15. Ijspeert, A. J. Amphibious and sprawling locomotion: from biology to robotics and back. Annu. Rev. Control Robot. Auton. Syst. 3, 091919–095731 (2020).

    Google Scholar 

  16. Nyakatura, J. A. et al. Reverse-engineering the locomotion of a stem amniote. Nature 565, 351–355 (2019).

    ADS  CAS  PubMed  Google Scholar 

  17. Mazouchova, N., Umbanhowar, P. B. & Goldman, D. I. Flipper-driven terrestrial locomotion of a sea turtle-inspired robot. Bioinspir. Biomim. 8, 026007 (2013).

    ADS  PubMed  Google Scholar 

  18. Crespi, A., Karakasiliotis, K., Guignard, A. & Ijspeert, A. J. Salamandra Robotica II: an amphibious robot to study salamander-like swimming and walking gaits. IEEE Trans. Robot. 29, 308–320 (2013).

    Google Scholar 

  19. Ijspeert, A. J. Biorobotics: using robots to emulate and investigate agile locomotion. Science 346, 196–203 (2014).

    ADS  CAS  PubMed  Google Scholar 

  20. Wyneken, J. in The Biology of Sea Turtles Vol. 1 (eds Lutz, P. L. & Musick, J. A.) Ch. 7 (CRC, 1997).

  21. Zani, P. A., Gottschall, J. S. & Kram, R. Giant Galapagos tortoises walk without inverted pendulum mechanical-energy exchange. J. Exp. Biol. 208, 1489–1494 (2005).

    PubMed  Google Scholar 

  22. Baines, R., Freeman, S., Fish, F. & Kramer-Bottiglio, R. Variable stiffness morphing limb for amphibious legged robots inspired by chelonian environmental adaptations. Bioinspir. Biomim. 15, 025002 (2020).

    ADS  PubMed  Google Scholar 

  23. Blob, R., Mayerl, C., Rivera, A., Rivera, G. & Young, V. On the fence versus all in: insights from turtles for the evolution of aquatic locomotor specializations and habitat transitions in tetrapod vertebrates. Integr. Comp. Biol. 56, 1310–1322 (2016).

    PubMed  PubMed Central  Google Scholar 

  24. Rivera, A. R. V., Wyneken, J. & Blob, R. W. Forelimb kinematics and motor patterns of swimming loggerhead sea turtles (Caretta caretta): are motor patterns conserved in the evolution of new locomotor strategies? J. Exp. Biol. 214, 3314–3323 (2011).

    PubMed  Google Scholar 

  25. Li, C., Umbanhowar, P. B., Komsuoglu, H., Koditschek, D. E. & Goldman, D. I. Sensitive dependence of the motion of a legged robot on granular media. Proc. Natl Acad. Sci. USA 106, 3029–3034 (2009).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Mazouchova, N., Gravish, N., Savu, A. & Goldman, D. I. Utilization of granular solidification during terrestrial locomotion of hatchling sea turtles. Biol. Lett. 6, 398–401 (2010).

    PubMed  PubMed Central  Google Scholar 

  27. Richefeu, V., El Youssoufi, M. S. & Radjai, F. Shear strength properties of wet granular materials. Phys. Rev. E 73, 051304 (2006).

    ADS  Google Scholar 

  28. Simoni, A. & Houlsby, G. T. The direct shear strength and dilatancy of sand and gravel mixtures. Geotech. Geol. Eng. 24, 523–549 (2006).

    Google Scholar 

  29. Kuo, A. D. Choosing your steps carefully. IEEE Robot. Autom Mag. 14, 18–29 (2007).

    MathSciNet  Google Scholar 

  30. White, C. H., Lauder, G. V. & Bart-Smith, H. Tunabot Flex: a tuna-inspired robot with body flexibility improves high-performance swimming. Bioinspir. Biomim. 16, 026019 (2021).

    Google Scholar 

  31. Mahadevan, L. Morphogenesis: Geometry, Physics, and Biology (Perimeter Institute for Theoretical Physics, 2021).

  32. Ultee, E., Ramijan, K., Dame, R. T., Briegel, A. & Claessen, D. Stress-induced adaptive morphogenesis in bacteria. Adv. Microb. Physiol. 74, 97–141 (2019).

  33. Justice, S. S., Hunstad, D. A., Cegelski, L. & Hultgren, S. J. Morphological plasticity as a bacterial survival strategy. Nat. Rev. Microbiol. 6, 162–168 (2008).

    CAS  PubMed  Google Scholar 

  34. Kim, S. Y. et al. Reconfigurable soft body trajectories using unidirectionally stretchable composite laminae. Nat. Commun. 10, 3464 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  35. Kinoshita, C., Fukuoka, T., Narazaki, T., Niizuma, Y. & Sato, K. Analysis of why sea turtles swim slowly: a metabolic and mechanical approach. J. Exp. Biol. 224, jeb236216 (2021).

    PubMed  Google Scholar 

  36. Butler, P., Milsom, W. & Woakes, A. Respiratory, cardiovascular and metabolic adjustments during steady state swimming in the green turtle, Chelonia mydas. J. Comp. Physiol. B 154, 167–174 (1984).

    Google Scholar 

  37. Baudinette, R. V., Miller, A. M. & Sarre, M. P. Aquatic and terrestrial locomotory energetics in a toad and a turtle: a search for generalisations among ectotherms. Physiol. Biochem. Zool. 73, 672–682 (2000).

    CAS  PubMed  Google Scholar 

  38. Madden, J. D. Mobile robots: motor challenges and materials solutions. Science 318, 1094–1097 (2007).

    ADS  CAS  PubMed  Google Scholar 

  39. Davenport, J. Locomotion in hatchling leatherback turtles Dermochelys coriacea. J. Zool. 212, 85–101 (1987).

    Google Scholar 

  40. Eckert, S. A. Swimming speed and patterns of leatherback turtles. J. Exp. Biol. 205, 3689–3697 (2002).

  41. Tucker, V. A. Energetic cost of locomotion in animals. Comp. Biochem. Physiol. 34, 841–846 (1970).

    CAS  PubMed  Google Scholar 

  42. Long, J. H., Schumacher, J., Livingston, N. & Kemp, M. Four flippers or two? Tetrapodal swimming with an aquatic robot. Bioinspir. Biomim. 1, 20–29 (2006).

    ADS  PubMed  Google Scholar 

  43. Chen, Y., Doshi, N., Goldberg, B., Wang, H. & Wood, R. J. Controllable water surface to underwater transition through electrowetting in a hybrid terrestrial–aquatic microrobot. Nat. Commun. 9, 2495 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  44. Wang, G. et al. Subsea crab bounding gait of leg-paddle hybrid driven shoal crablike robot. Mechatronics 48, 1–11 (2017).

    Google Scholar 

  45. Sellers, W. I., Rose, K. A., Crossley, D. A. & Codd, J. R. Inferring cost of transport from whole-body kinematics in three sympatric turtle species with different locomotor habits. Comp. Biochem. Physiol. A 247, 110739 (2020).

    CAS  Google Scholar 

  46. Milana, E. et al. EELWORM: a bioinspired multimodal amphibious soft robot. In 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft) 766–771 (IEEE, 2020).

  47. Kitano, S., Hirose, S., Horigome, A. & Endo, G. TITAN-XIII: sprawling-type quadruped robot with ability of fast and energy-efficient walking. ROBOMECH J. 3, 8 (2016).

    Google Scholar 

  48. Kandhari, A., Wang, Y., Chiel, H. J., Quinn, R. D. & Daltorio, K. A. An analysis of peristaltic locomotion for maximizing velocity or minimizing cost of transport of earthworm-like robots. Soft Robot. 8, 485–505 (2021).

    PubMed  Google Scholar 

  49. Kau, N., Schultz, A., Ferrante, N. & Slade, P. Stanford Doggo: an open-source, quasi-direct-drive quadruped. In 2019 International Conference on Robotics and Automation (ICRA) 6309–6315 (IEEE, 2019).

  50. Berlinger, F., Saadat, M., Haj-Hariri, H., Lauder, G. V. & Nagpal, R. Fish-like three-dimensional swimming with an autonomous, multi-fin, and biomimetic robot. Bioinspir. Biomim. 16, 026018 (2021).

    Google Scholar 

  51. Kim, K., Spieler, P., Lupu, E.-S., Ramezani, A. & Chung, S.-J. A bipedal walking robot that can fly, slackline, and skateboard. Sci. Robot. 6, eabf8136 (2021).

    PubMed  Google Scholar 

  52. Bledt, G. et al. MIT Cheetah 3: design and control of a robust, dynamic quadruped robot. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2245–2252 (IEEE, 2018).

  53. Hutter, M. et al. ANYmal—a highly mobile and dynamic quadrupedal robot. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 38–44 (IEEE, 2016).

  54. Craig, J. J. Introduction to Robotics: Mechanics & Control (Addison-Wesley, 1986).

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Acknowledgements

We thank V. Wilczynski, Deputy Dean of Yale School of Engineering, for use of his pool. This project was sponsored by the Office of Naval Research under award N00014-21-1-2417. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Office of Naval Research. R.B. was supported by an NSF Graduate Research Fellowship (DGE-1752134).

Author information

Authors and Affiliations

Authors

Contributions

R.B., S.K.P., J.B., T.S. and A.G. designed and built the robot. R.B., S.K.P. and L.R. conducted swimming, terrestrial and transition experiments. R.B., S.K.P., L.R. and F.F. programmed gaits. R.B. conducted CFD simulations and friction tests. R.K.-B. conceived of the project and oversaw the research. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Rebecca Kramer-Bottiglio.

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The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Navinda Kottege and Cecilia Laschi for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 System description.

a, Exploded computer-aided-design view, detailing components of robot. Note that the shoulder joints are typically shrouded in rubber bellows, and thus not visible. For clarity, we only depict the bellows on the back right joint in transparent grey. b, 3-DoF kinematic mechanism used to achieve bio-inspired gaits. Symbols θ, ϕ and α are the rotation axes for the swing forward/backward, up/down, and angle of attack motors, respectively.

Extended Data Fig. 2 Robot workspace.

Workspace visualized in the robot’s kinematic simulation. The explored gaits are superimposed atop the cyan workspace in a darker colour. Top two are swimming gaits; bottom two are terrestrial and transition gaits. Chassis beneath the shell is rendered for context. Links are line segments coloured red, green, and blue. The black arrow points in the forward travel direction.

Extended Data Fig. 3 Fabrication of Morphing Limb.

Fabrication can be broken down into two main tracks: elastomeric actuators (top), and Joule-heating variable-stiffness material (bottom). We fabricate components for each of the limbs’ two halves, A and B. Actuators and variable-stiffness material components for A and B come together in the final step, in which they are hinged together via a sewed joint. The limb cross-section is displayed in the inset. Figure and caption adapted from ref. 22.

Extended Data Fig. 4 Test rig for evaluating force profiles of gaits.

Coordinate system defines positive direction of forces measured via the multi-axis load cell. Adjustable fixtures allowed us to tune the robot’s offset from the pool sides and bottom, as well as its submerged depth.

Extended Data Fig. 5 Representative data for front-right shoulder during paddling and flapping gaits.

Top row gives a robot schematic with superimposed trajectory. Second row gives commanded and actual encoder positions, where the solid line is the actual achieved position. Third row plots the angular velocity. Fourth row is the amperage. Line colour and legend symbols match those of Fig. 1b.

Extended Data Fig. 6 CFD results.

Simulation results of lift and drag forces on the flipper mode of the morphing limb, at 0.3 m/s. Inset depicts definition of Φ with respect to the flow direction.

Extended Data Fig. 7 Representative data from all shoulder joints for creeping gait on the porcelain substrate.

Top row presents snapshots of the robot at key parts of the gait. Graphs underneath give commanded and actual encoder positions, where the solid line is the actual achieved position. Line colour and legend symbols match those of Fig. 1b.

Extended Data Fig. 8 Additional creep data.

a, Left to right: commanded and actual encoder positions, angular velocity, and amperage for front-right shoulder joint. The solid line is the actual achieved position. Line colour and legend symbols match those of Fig. 1b. b, Representative data from motion capture of back-left distal tip of limb during creep gait. Spikes in Z correspond to the limb swinging out, whereas the other portions are the pivot or where the limb serves only to balance the robot. Notice the amplitude and frequency of vibrations occurring at these times, which give an indication of surface normals and roughness. At these sections, we calculated the stability metric to grasp the effect of substrates on COT when creeping. Here, the series are not normalized to all start at 0, for ease of viewing.

Extended Data Fig. 9 Representative data from front-right shoulder joint for crawl gaits across the three substrates.

Top row gives a robot schematic with superimposed trajectory. Second row gives commanded and actual encoder positions, where the solid line is the actual achieved position. Third row plots the angular velocity. Fourth row is the amperage. Line colour and legend symbols match those of Fig. 1b.

Extended Data Fig. 10 Example data from friction tests.

Force versus displacement for elastomer, PLA, or bellows with 500 g weight placed atop them, over the various substrates. Clouds indicate one standard deviation from the mean, over 7 trials, with the solid line as the mean.

Extended Data Table 1 The robot’s modified Denavit–Hartenberg parameters54
Extended Data Table 2 The robot’s velocities and COTs for all gaits
Extended Data Table 3 Experimentally determined friction coefficients between ART’s constituent materials and the various tested substrates

Supplementary information

Peer Review File

Supplementary Video 1

Aquatic testing. We demonstrate how ART can perform multiple types of aquatic gait using the hydrodynamically favourable flipper shape.

Supplementary Video 2

Land locomotion. ART is capable of traversing multiple substrates. Here we show footage from porcelain, concrete and granite substrates, both in the lab and outdoors on Yale campus.

Supplementary Video 3

Transition-substrate tests. An upright creep gait lacks stability on granular and highly fluidized media. ART adapts to a splayed crawling gait, distributing its weight, to traverse such substrates.

Supplementary Video 4

Morphing sequence. We demonstrate how the morphing limb undergoes drastic, reversible shape change.

Supplementary Video 5

Variable-stiffness composite functionality underwater. The morphing limb is waterproof and able to switch between leg and flipper modes underwater.

Supplementary Video 6

Transition field testing. We combined favourable shape-gait policies from terrestrial and aquatic environments to study terrestrial-to-aquatic transitions at an ocean inlet.

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Baines, R., Patiballa, S.K., Booth, J. et al. Multi-environment robotic transitions through adaptive morphogenesis. Nature 610, 283–289 (2022). https://doi.org/10.1038/s41586-022-05188-w

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