HaomingLuo/AgentFEM-Programmable-Soft-Rods

AgentFEM · Programmable Soft Rods

New: multi-zone actuation supplement — 4,080 additional states; 3/6/12 zones, with 8-zone holdout and mesh checks. The original 16,200-state collection remains available as equilibria.

Learn shapes. Distinguish branches. Design actuation.

216 physical scenarios · 648 equilibrium sweeps · 16,200 states

Planar flexible rods driven by end loads, three-zone intrinsic curvature, and microscale forces. Each state contains its centerline, angle and strain fields, control parameters, local stability, and solver evidence.

Models & solver · AgentFEM · Mechanics & schema

What can you study?

• Shape prediction under different actuation mechanisms.
• Multi-solution regression: identical loading can admit straight, up-bent and down-bent states.
• Bounded inverse design of endpoint positions or complete centerlines.
• Resolution sensitivity and local equilibrium stability.

This is the first data release of AgentFEM Morph Lab, a project on programmable flexible structures. The JMPS microforce rod is one featured mechanism, alongside conventional loading and distributed eigen-curvature actuation.

Contents

Collection	Included
End-force / end-moment rods	72 scenarios, 5,400 equilibrium samples
Three-zone intrinsic-curvature rods	72 scenarios, 5,400 equilibrium samples
Microforce rods	72 scenarios, 5,400 samples across up/down/straight branches
Paired resolution checks	216 pairs, 48 vs 96 angle elements
Native AgentFEM solids	3 independent Figure 9 cases, actual downward solutions
Reproducibility	Source, tests, schema, numerical reports, hashes

The 1D corpus is produced by the supplied NumPy/SciPy angle-FE solver. The three 2D checks use native AgentFEM/FEniCSx. Sample frames are not independent scenarios; reversible sweeps can revisit the same shape.

Load a sample

from datasets import load_dataset
data = load_dataset("HaomingLuo/AgentFEM-Programmable-Soft-Rods", "equilibria")
sample = data["test"][0]
print(sample["controls"], sample["tip"], sample["stability"])

Without the datasets library:

import gzip, json
from huggingface_hub import hf_hub_download
path = hf_hub_download("HaomingLuo/AgentFEM-Programmable-Soft-Rods",
                      "states/test.jsonl.gz", repo_type="dataset")
with gzip.open(path, "rt") as f:
    sample = json.loads(next(f))

Validation

• Axial extension and constant-curvature analytic checks pass.
• Energy gradient and tangent verified by finite differences.
• First continuum bifurcation load: absolute discrepancy 1.09e-8 in dimensionless p.
• Figure 9 independent BVP vs 96-element rod: centerline RMSE below 5.87e-5 L.
• All 216 resolution pairs: maximum centerline RMSE 1.72e-4 L.
• Three 2D equivalent-solid vs paper-continuum errors: 0.758%, 0.176%, 0.394% of L.
• PVD and XDMF both opened and warped in ParaView; all three fields share one geometry.

Full evidence: validation, generation, native FE metrics, ParaView checks. These measurements apply to the stated checks, not an accuracy guarantee for arbitrary rods.

Splits and baselines

126 training / 27 validation / 27 test / 36 stiffness-OOD scenes. All sweeps, mirrored branches and mesh pairs of a scene stay in the same partition. The test split was not used for fitting or checkpoint selection; there is no sealed test or geometry-OOD benchmark in this release.

Predictor	Supplied branch?	Test centerline RMSE / L
Ordinary MLP	No	0.27097
Branch-conditioned nearest neighbor	Yes	0.13899
Branch-conditioned MLP	Yes	0.00661

The ordinary MLP receives ambiguous multi-solution inputs; this is an input identifiability demonstration, not proof of a superior neural architecture. The conditioned MLP's mean test endpoint error is 0.00784 L. Stiffness-OOD centerline RMSE is 0.00941 L. See all baseline results.

Physical scope

Dimensionless, conservative planar rods; fixed clamp; force control. Stability labels come from the constrained discrete energy Hessian. Sweeps are quasi-static equilibria, not physical-time trajectories. The supplied inverse examples use bounded intrinsic curvature and numerical forward verification. They do not establish general reachability, snap-through dynamics, contact or robot hardware control.

See SCHEMA.md for equations, normalization, sampling, branch semantics, and the difference between the paper's end force p and effective resultant p+2h.

中文

可编程柔性杆：从形状预测到驱动设计。

首批包含三类问题：端力／端矩驱动、三段本征曲率驱动，以及 JMPS 论文中的 微结构驱动。216 个独立场景、648 条平衡状态扫描、16,200 个采样状态；另有 216 组网格加密对照和 3 个 AgentFEM 二维实体核验算例。

数据可用于形状预测、多解回归和逆向设计。上下弯分支及同场景的不同路径不跨 训练／测试划分。三段本征曲率是一种理想化驱动模型；当前不含真实跳跃动力学、 接触或硬件实验。二维结果实际求解得到下弯形状，未在绘图时翻转。

下载 native_fe/paraview/ 后打开 PVD，使用 U 做 Warp By Vector；XDMF 的 位移为 P1 插值副本，完整 Q2 系数另存于 native_fe/scientific/。

Reproduce and cite

Version 0.1.0, seed 20261014. Install requirements.txt, then follow SCHEMA.md. Native FE additionally requires AgentFEM/FEniCSx.

Data and generated figures: CC BY 4.0. Code: Apache-2.0. The paper is cited, not redistributed or relicensed.

Theory reference: Yang Ye, Andrea Piccolroaz, Davide Bigoni, Three Homogenization Models for a Nonlinear Heterogeneous Rod with Microscale Dead Loading, DOI: 10.1016/j.jmps.2026.106870.

Please cite Haoming Luo, AgentFEM Morph Lab: Programmable Soft Rods, v0.1.0 (2026), the dataset URL and revision, and the theory reference when using the microforce cases.