Learning, locomotion, and navigation of soft synthetic snakes in three-dimensional, heterogeneous environments
This work provides a simulation platform and control insights for continuum robots operating in natural terrains, though the approach is incremental.
The authors developed a reinforcement learning framework for soft synthetic snakes to navigate unstructured 3D terrains, achieving reliable navigation in high-fidelity environments reconstructed from real-world imaging.
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.