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Sim-to-real transfer of co-optimized soft robot crawlers

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Abstract

This work provides a complete framework for the simulation, co-optimization, and sim-to-real transfer of the design and control of soft legged robots. Soft robots have “mechanical intelligence”: the ability to passively exhibit behaviors that would otherwise be difficult to program. Exploiting this capacity requires consideration of the coupling between design and control. Co-optimization provides a way to reason over this coupling. Yet, it is difficult to achieve simulations that are both sufficiently accurate to allow for sim-to-real transfer and fast enough for contemporary co-optimization algorithms. We describe a modularized model order reduction algorithm that improves simulation efficiency, while preserving the accuracy required to learn effective soft robot design and control. We propose a reinforcement learning-based co-optimization framework that identifies several soft crawling robots that outperform an expert baseline with zero-shot sim-to-real transfer. We study generalization of the framework to new terrains, and the efficacy of domain randomization as a means to improve sim-to-real transfer.

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Notes

  1. A publicly available implementation of our joint optimization framework is available at https://github.com/cbschaff/evolving-soft-robots.

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Acknowledgements

We thank Olivier Goury and Hugo Talbot for assistance with the SOFA implementation, and Arthur MacKeith.

Funding

Partially funded by the National Sciences and Engineering Research Council of Canada (NSERC), Discovery Grants program (RGPIN 1512).

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Author notes

  1. C. Schaff and A. Sedal have contributed equally to this work.

Authors and Affiliations

  1. Department of Mechanical Engineering, McGill University, 817 Sherbrooke St W, Montreal, QC, H3A 0C3, Canada

    Audrey Sedal & Shiyao Ni

  2. TTI-Chicago, 6045 S Kenwood Ave, Chicago, IL, 60637, USA

    Charles Schaff & Matthew R. Walter

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  1. Charles Schaff
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  2. Audrey Sedal
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Contributions

AS and CS contributed equally to this manuscript. AS, CS and MW wrote the text. AS, MW and CS developed the framework and performed physical experiments. CS and SN performed computations. SN generated the video. All authors reviewed the manuscript.

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Correspondence to Audrey Sedal.

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Code is available at the framework’s Github Page: https://github.com/macrobotics-lab/evolving-soft-robots/tree/main. Data is available at https://github.com/macrobotics-lab/AuRo_evolving_sobot_data.

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Schaff, C., Sedal, A., Ni, S. et al. Sim-to-real transfer of co-optimized soft robot crawlers. Auton Robot 47, 1195–1211 (2023). https://doi.org/10.1007/s10514-023-10130-8

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