Learning-Based Inverse Design for Soft Modular Robots
Overview
This research presents a learning-based real-to-sim-to-real framework that translates biological curves into robotic configurations through surrogate-assisted inverse design and uncertainty-guided refinement. The method reduces deformation prediction time from 1.53 × 10⁵ ms to 0.0008 ms and design time from 1.99 × 10⁶ ms to 4.18 ms. It achieves a 93% shape reconstruction success rate on 5,000 test samples and is validated through physical demonstrations with four bioinspired robots.
From arXiv Robotics — research abstracts
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · deformation prediction time: 0.0008 other · Basis not reported
“refinement. The proposed method combines learned inverse prediction with targeted candidate exploration, using a fast forward surrogate to evaluate and select suitable designs. The learned forward surrogate reduces deformation prediction time from $1.53 \times 10^5$ ms to 0.0008 ms, achieving approximately a $1.9 \times 10^8$-fold speedup. At the same time, the proposed inverse design method reduces design time from”
Exact source · revision 2Source owner not reported
Reported quantity · design time: 4.18 other · Basis not reported
“$1.99 \times 10^6$ ms to 4.18 ms. On 5,000 test samples generated from previously unseen threading patterns, the method achieves a 93% shape reconstruction success rate under surrogate-based evaluation. Physical demonstrations with four bioinspired robots further illustrate the translation of biological curves into realizable robotic shapes.”
Exact source · revision 2Source owner not reported
Reported quantity · test samples: 5000 other · Basis not reported
“$1.99 \times 10^6$ ms to 4.18 ms. On 5,000 test samples generated from previously unseen threading patterns, the method achieves a 93% shape reconstruction success rate under surrogate-based evaluation. Physical demonstrations with four bioinspired robots further illustrate the translation of biological curves into realizable robotic shapes.”
Exact source · revision 2Source owner not reported
Reported quantity · forward surrogate prediction time: 0.0008 other · Basis not reported
“refinement. The proposed method combines learned inverse prediction with targeted candidate exploration, using a fast forward surrogate to evaluate and select suitable designs. The learned forward surrogate reduces deformation prediction time from $1.53 \times 10^5$ ms to 0.0008 ms, achieving approximately a $1.9 \times 10^8$-fold speedup. At the same time, the proposed inverse design method reduces design time from”
Exact source · revision 2Source owner not reported
Evidence dependency changes (1)
- 2026-10-30T17:43:48.012Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalFrom Biological Curves to Soft Modular Robots: Learning-Based Inverse Design with Uncertainty-Guided Refinement
This research presents a learning-based real-to-sim-to-real framework that translates biological curves into robotic configurations through surrogate-assisted inverse design and uncertainty-guided refinement. The method reduces deformation prediction time from 1.53 × 10⁵ ms to 0.0008 ms and design time from 1.99 × 10⁶ ms to 4.18 ms. It achieves a 93% shape reconstruction success rate on 5,000 test samples and is validated through physical demonstrations with four bioinspired robots.
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