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.

翻译：深度学习在科学发现领域展现出巨大潜力，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。本研究针对空间超约束机构展开分析，提出端到端框架O-ConNet——该框架仅需三个稀疏可达点即可推断机构结构参数，同时重构完整运动轨迹，而无需在推断过程中显式求解约束方程。在自建的包含42,860个有效样本的Bennett 4R数据集中，O-ConNet的Param-MAE达到0.276±0.077，Traj-MAE达到0.145±0.018（10次运行结果的均值±标准差），分别较最优序列基线模型（LSTM-Seq2Seq）提升65.1%和88.2%。实验结果表明，端到端学习能够捕获闭环几何结构，为在极度稀疏观测条件下实现空间超约束机构的逆向设计提供了可行路径。