Article

From Biological Curves to Modular Soft Robots: Learning-Based Inverse Design with Uncertainty-Guided Refinement

The paper proposes a learning-based real-to-sim-to-real framework that converts biological curves into cable-driven modular soft robot configurations through surrogate-assisted inverse design and uncertainty-guided refinement. The learned forward surrogate reduces deformation prediction time from 1.53×10^5 ms to 0.0008 ms, a speedup of about 1.9×10^8 times; inverse design time drops from 1.99×10^6 ms to 4.18 ms.

Part of the event: Learning-based inverse design framework turns biological curves into soft robot configurations

This page provides a summary and source link only.