O-ConNet: Geometry-Aware End-to-End Inference of Over-Constrained Spatial Mechanisms
Hey there, little scientist! Guess what? Smart computer brains, like a super-duper detective, are learning new tricks!
Imagine you have a special toy, like a robot arm or a cool pop-up book. This toy moves in a very specific way, like a dance!
Scientists made a new smart computer brain called O-ConNet. It's like a super-smart guesser! You show it just three tiny parts of the toy's dance, and it can guess all the secret parts of the toy and all the other dance moves it can do!
It's like magic, but it's really smart computers helping us understand how cool moving things work! Yay for O-ConNet!
arXiv:2604.02038v1 Announce Type: new 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 res
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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.
Comments: 8 pages, 5 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2604.02038 [cs.RO]
(or arXiv:2604.02038v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2604.02038
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Haoyu Sun [view email] [v1] Thu, 2 Apr 2026 13:43:53 UTC (12,943 KB)
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