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Learning, locomotion, and navigation of soft synthetic snakes in three-dimensional, heterogeneous environments
Xiaotian Zha · 2026-05-26 · via cs.LG updates on arXiv.org

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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.
Comments: 14 pages, 5 figures
Subjects: Robotics (cs.RO); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
Cite as: arXiv:2605.24985 [cs.RO]
  (or arXiv:2605.24985v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2605.24985

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Xiaotian Zhang [view email]
[v1] Sun, 24 May 2026 10:24:18 UTC (24,005 KB)