O-ConNet: Geometry-Aware End-to-End Inference of Over-Constrained Spatial Mechanisms

• URL: http://arxiv.org/abs/2604.02038v1
• Date: Thu, 02 Apr 2026 13:43:53 GMT
• Title: O-ConNet: Geometry-Aware End-to-End Inference of Over-Constrained Spatial Mechanisms
• Abstract summary: O-ConNet is an end-to-end framework that infers mechanism structural parameters from only three sparse reachable points.<n>O-ConNet achieves Param-MAE 0.276 +/- 0.077 and Traj-MAE 0.145 +/- 0.018 on a self-constructed Bennett 4R dataset.<n>These results suggest that end-to-end learning can capture closed-loop structure and provide a practical route for inverse design of spatial over-constrained mechanisms.
• Score: 6.353145241135741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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