Abstract
Deep learning has shown strong potential for scientific discovery, but its ability to model macroscopic rigid-body kinematic constraints remains underexplored. We study this problem on spatial over-constrained mechanisms and propose O-ConNet, an end-to-end framework that infers mechanism structural parameters from only three sparse reachable points while reconstructing the full motion trajectory, without explicitly solving constraint equations during inference. On a self-constructed Bennett 4R dataset of 42,860 valid samples, O-ConNet achieves Param-MAE 0.276 +/- 0.077 and Traj-MAE 0.145 +/- 0.018 (mean +/- std over 10 runs), outperforming the strongest sequence baseline (LSTM-Seq2Seq) by 65.1 percent and 88.2 percent, respectively. These results suggest that end-to-end learning can capture closed-loop geometric structure and provide a practical route for inverse design of spatial over-constrained mechanisms under extremely sparse observations.
Abstract (translated)
深度学习在科学发现中展现出巨大潜力，但其对宏观刚体运动学约束的建模能力仍未得到充分探索。本研究针对空间过约束机构开展研究，提出O-ConNet端到端框架，该框架仅通过三个稀疏可达点即可推断机构结构参数并重建完整运动轨迹，且在推理过程中无需显式求解约束方程。在自建的本特利4R机构数据集（42,860个有效样本）上，O-ConNet达到参数平均绝对误差0.276±0.077、轨迹平均绝对误差0.145±0.018（十次运行均值±标准差），分别超越最强序列基线模型（LSTM-Seq2Seq）达65.1%和88.2%。这些结果表明，端到端学习能够捕捉闭环几何结构，并为极端稀疏观测条件下的空间过约束机构逆设计提供了实用途径。
URL
https://arxiv.org/abs/2604.02038
