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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2605.24985 |
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| _version_ | 1866917529130106880 |
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| author | Zhang, Xiaotian Albazroun, Ali Wang, Tixian Cui, Songyuan Mehta, Prashant G. Gazzola, Mattia |
| author_facet | Zhang, Xiaotian Albazroun, Ali Wang, Tixian Cui, Songyuan Mehta, Prashant G. Gazzola, Mattia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24985 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning, locomotion, and navigation of soft synthetic snakes in three-dimensional, heterogeneous environments Zhang, Xiaotian Albazroun, Ali Wang, Tixian Cui, Songyuan Mehta, Prashant G. Gazzola, Mattia Robotics Machine Learning Computational Physics 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. |
| title | Learning, locomotion, and navigation of soft synthetic snakes in three-dimensional, heterogeneous environments |
| topic | Robotics Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2605.24985 |