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

2026-05-24

AI Summary

This work addresses the challenge of enabling soft-bodied synthetic snakes to achieve efficient locomotion and navigation in complex, unstructured, three-dimensional heterogeneous environments. To this end, the authors propose a hierarchical reinforcement learning framework that integrates biologically inspired actuation and perception models. The approach employs a staged training paradigm: first learning fundamental motion primitives in simplified terrains, then composing these primitives into adaptive navigation policies for complex environments, thereby significantly reducing the control complexity inherent in high-degree-of-freedom continuum systems. Validated on a high-fidelity 3D simulation platform built from real-world scene reconstructions, the method demonstrates robust and adaptive traversal capabilities across diverse natural terrains, confirming its practicality and resilience.

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.

Problem

• soft robotics
• locomotion
• navigation
• heterogeneous environments
• 3D terrains

Innovation

• soft robotics
• reinforcement learning
• bio-inspired actuation
• continuum systems
• 3D terrain navigation