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Versatile modular neural locomotion control with fast learning

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A preprint version of the article is available at arXiv.

Abstract

Legged robots have significant potential to operate in unstructured environments. The design of locomotion control is, however, still challenging. Currently, controllers must be manually designed for specific robots and tasks, or automatically designed via machine learning methods that require long training times and yield large opaque controllers. Drawing inspiration from animal locomotion, we propose a simple yet versatile modular neural control structure with fast learning. The key advantages of our approach are that behaviour-specific control modules can be added incrementally to obtain increasingly complex emergent locomotion behaviours, and that neural connections can be quickly and automatically learned. In a series of experiments, we show how eight modules can be quickly learned and added to a base control module to obtain emergent adaptive behaviours allowing a hexapod robot to navigate in complex environments. We also show that modules can be added and removed during operation without affecting the functionality of the remaining controller. Finally, the controller is successfully demonstrated on a physical robot. Taken together, our study reveals a significant step towards fast automatic design of versatile neural locomotion control.

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Fig. 1: Overview of the versatile modular neural locomotion controller.
Fig. 2: Results of learning the base and obstacle reflex controllers.
Fig. 3: Results of learning the body posture and directional locomotion controllers.
Fig. 4: Results for using the learned primitive control modules.
Fig. 5: The advanced behaviours and the results for using all eight control modules.
Fig. 6: Detailed overview of the versatile modular neural locomotion controller.

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Data availability

All data from running the experiments as well as the learned weight sets can be accessed at https://github.com/MathiasThor/CPG-RBFN-framework/tree/main/data50.

Code availability

The source code for running the controller in simulation can be accessed at https://github.com/MathiasThor/CPG-RBFN-framework50.

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Acknowledgements

We thank the members of the SDU Biorobotics group for their technical support and helpful discussions. We also thank A. L. Christensen for fruitful discussions and detailed feedback. This work was supported in part by the Horizon 2020 Framework Programme (FETPROACT-01-2016 FET Proactive: Emerging Themes and Communities) under grant 732266 (Plan4Act) (P.M., project workpackage principal investigator) and in part by a start-up grant on Bio-inspired Robotics from the Vidyasirimedhi Institute of Science and Technology (VISTEC) (P.M., project principal investigator).

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Authors and Affiliations

  1. Embodied AI and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, The University of Southern Denmark, Odense, Denmark

    Mathias Thor & Poramate Manoonpong

  2. Bio-Inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand

    Poramate Manoonpong

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  1. Mathias Thor
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Contributions

M.T. contributed to the modular neural control structure, implementation, experiments, data analysis and the manuscript. P.M. contributed to the embodied neural control structure, data analysis and the manuscript.

Corresponding author

Correspondence to Mathias Thor.

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The authors declare no competing interests.

Peer review

Peer review information

Nature Machine Intelligence thanks Luca Patané, Fernando Perez-Peña and Ren Qinyuan for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Sections 1–6, Figs. 1–9, Tables 1 and 2, and Algorithm 1.

Supplementary Video 1

Learning the base controller.

Supplementary Video 2

Learning the obstacle reflex controller.

Supplementary Video 3

Learning the body posture controller.

Supplementary Video 4

Learning the directional locomotion controller.

Supplementary Video 5

Deploying primitive controllers in simulation.

Supplementary Video 6

Deploying primitive controllers on a physical robot.

Supplementary Video 7

Deploying primitive and advanced controllers in simulation.

Supplementary Video 8

Controller generalization and limitations.

Supplementary Video 9

Disabling primitive control modules.

Supplementary Video 10

Disabling advanced control modules.

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Thor, M., Manoonpong, P. Versatile modular neural locomotion control with fast learning. Nat Mach Intell 4, 169–179 (2022). https://doi.org/10.1038/s42256-022-00444-0

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