Shape-Interpretable Visual Self-Modeling Enables Geometry-Aware Continuum Robot Control
Continuum robots possess high flexibility and redundancy, making them well suited for safe interaction in complex environments, yet their continuous deformation and nonlinear dynamics pose fundamental challenges to perception, modeling, and control. Existing vision-based control approaches often rely on end-to-end learning, achieving shape regulation without explicit awareness of robot geometry or its interaction with the environment. Here, we introduce a shape-interpretable visual self-modeling framework for continuum robots that enables geometry-aware control. Robot shapes are encoded from multi-view planar images using a Bezier-curve representation, transforming visual observations int...
| Authors | Peng Yu, Xin Wang, Ning Tan |
|---|---|
| Primary category | cs.RO |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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