Datasets:
license: cc-by-4.0
language:
- en
- zh
task_categories:
- other
size_categories:
- 10K<n<100K
tags:
- agentfem
- soft-robotics
- programmable-matter
- structural-mechanics
- inverse-design
- scientific-machine-learning
pretty_name: AgentFEM Programmable Soft Rods
configs:
- config_name: multizone_v02
data_files:
- split: train
path: multizone-v02/train.jsonl.gz
- split: validation
path: multizone-v02/validation.jsonl.gz
- split: test
path: multizone-v02/test.jsonl.gz
- split: zone_ood
path: multizone-v02/zone_ood.jsonl.gz
- config_name: equilibria
data_files:
- split: train
path: states/train.jsonl.gz
- split: validation
path: states/validation.jsonl.gz
- split: test
path: states/test.jsonl.gz
- split: parameter_ood
path: states/parameter_ood.jsonl.gz
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.