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arXiv · 2605.24985

Learning, locomotion, and navigation of soft synthetic snakes in three-dimensional, heterogeneous environments

Xiaotian Zhang·Ali Albazroun·Tixian Wang·Songyuan Cui·Prashant G. Mehta·Mattia Gazzola
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Abstract

Limbless terrestrial animals exhibit exceptional locomotor versatility and control, currently unmatched by engineered counterparts. Here, we introduce a computational framework that enables soft synthetic snakes to navigate unstructured, heterogeneous 3D terrains. Our approach is grounded in bio-inspired actuation and sensing models that reduce the control complexity inherent to high-degree-of-freedom, continuum bodies. These models are integrated into a reinforcement learning architecture to derive environment-traversing policies. Training first occurs in simplified, homogeneous terrains to learn locomotion primitives. These are then composed into adaptive strategies for complex landscapes. We demonstrate robustness by deploying a snake in high-fidelity 3D environments reconstructed from real-world imaging, achieving reliable navigation. Overall, this work provides a physically-realistic simulation platform and practical insights for the control of continuum systems in natural terrains.

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Xiaotian Zhang, Ali Albazroun, Tixian Wang, Songyuan Cui, Prashant G. Mehta, Mattia Gazzola. 2026-05-24. Learning, locomotion, and navigation of soft synthetic snakes in three-dimensional, heterogeneous environments. https://arxiv.org/abs/2605.24985

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