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Real-world embodied AI through a morphologically adaptive quadruped robot


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.

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Fig. 1: The morphologically adaptive robot used in this study.
Fig. 2: Environments and results for indoor and outdoor experiments.
Fig. 3: The COT of different leg lengths on the three terrains.
Fig. 4: Diagram showing the two adaptation methods used.
Fig. 5: Analysis of the adaptation algorithm.
Fig. 6: Model transformation during adaptation.

Data availability

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.

Code availability

The code for running the robot is available on the DyRET Github page51, and is under a GPL v.3 license. The code to set-up the experiments and log the results is available is a separate Github repository58. Author contributions classified in the CRediT taxonomy59.


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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.

Author information




T.F.N. was responsible for conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, visualization, and reviewing and editing the original draft. C.P.M. was responsible for conceptualization, methodology, formal analysis and supervision. J.T. was responsible for supervision and funding acquisition. K.G. was responsible for conceptualization, methodology and supervision. D.H. was responsible for conceptualization, methodology, formal analysis, resources, visualization, supervision, project administration and funding acquisition. C.P.M., J.T., K.G. and D.H. all reviewed and edited the manuscript.

Corresponding author

Correspondence to Tønnes F. Nygaard.

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Competing interests

The authors declare no competing interests.

Additional information

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

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.

Extended Data Fig. 2 Reconfigurable mechanism and workspace difference.

a, The components of the mechanical adaptation mechanism. b, An indication of the difference in workspace for the two extreme leg lengths.

Extended Data Fig. 3 Terrain features for indoor boxes.

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.

Extended Data Fig. 4 Effect of speed on terrain measurements.

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.

Extended Data Fig. 5 One walking sequence on the indoor terrains.

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.

Supplementary information

Supplementary Information

Supplementary Methods and Tables 1–5.

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Nygaard, T.F., Martin, C.P., Torresen, J. et al. Real-world embodied AI through a morphologically adaptive quadruped robot. Nat Mach Intell (2021).

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