CableRobotGraphSim: A Graph Neural Network for Modeling Partially Observable Cable-Driven Robot Dynamics

Nelson Chen, William R. Johnson III, Rebecca Kramer-Bottiglio, Kostas Bekris, Mridul Aanjaneya
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:169-182, 2026.

Abstract

General-purpose simulators have accelerated the development of robots. Traditional simulators based on first-principles, however, typically require full-state observability or depend on parameter search for system identification. This work presents CableRobotGraphSim, a novel Graph Neural Network (GNN) model for cable-driven robots that aims to address shortcomings of prior simulation solutions. By representing cable-driven robots as graphs, with the rigid-bodies as nodes and the cables and contacts as edges, this model can quickly and accurately match the properties of other simulation models and real robots, while ingesting only partially observable inputs. Furthermore, trajectory rollout accuracy and inference speed are enhanced with prediction chunks, simultaneous multistep forward prediction. Accompanying the GNN model is a sim-and-real co-training procedure that promotes generalization and robustness to noisy real data. This model is further integrated with a Model Predictive Path Integral (MPPI) controller for closed-loop navigation, which showcases the model’s speed and accuracy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-chen26a, title = {CableRobotGraphSim: A Graph Neural Network for Modeling Partially Observable Cable-Driven Robot Dynamics}, author = {Chen, Nelson and III, William R. Johnson and Kramer-Bottiglio, Rebecca and Bekris, Kostas and Aanjaneya, Mridul}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {169--182}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/chen26a/chen26a.pdf}, url = {https://proceedings.mlr.press/v331/chen26a.html}, abstract = {General-purpose simulators have accelerated the development of robots. Traditional simulators based on first-principles, however, typically require full-state observability or depend on parameter search for system identification. This work presents CableRobotGraphSim, a novel Graph Neural Network (GNN) model for cable-driven robots that aims to address shortcomings of prior simulation solutions. By representing cable-driven robots as graphs, with the rigid-bodies as nodes and the cables and contacts as edges, this model can quickly and accurately match the properties of other simulation models and real robots, while ingesting only partially observable inputs. Furthermore, trajectory rollout accuracy and inference speed are enhanced with prediction chunks, simultaneous multistep forward prediction. Accompanying the GNN model is a sim-and-real co-training procedure that promotes generalization and robustness to noisy real data. This model is further integrated with a Model Predictive Path Integral (MPPI) controller for closed-loop navigation, which showcases the model’s speed and accuracy.} }
Endnote
%0 Conference Paper %T CableRobotGraphSim: A Graph Neural Network for Modeling Partially Observable Cable-Driven Robot Dynamics %A Nelson Chen %A William R. Johnson III %A Rebecca Kramer-Bottiglio %A Kostas Bekris %A Mridul Aanjaneya %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-chen26a %I PMLR %P 169--182 %U https://proceedings.mlr.press/v331/chen26a.html %V 331 %X General-purpose simulators have accelerated the development of robots. Traditional simulators based on first-principles, however, typically require full-state observability or depend on parameter search for system identification. This work presents CableRobotGraphSim, a novel Graph Neural Network (GNN) model for cable-driven robots that aims to address shortcomings of prior simulation solutions. By representing cable-driven robots as graphs, with the rigid-bodies as nodes and the cables and contacts as edges, this model can quickly and accurately match the properties of other simulation models and real robots, while ingesting only partially observable inputs. Furthermore, trajectory rollout accuracy and inference speed are enhanced with prediction chunks, simultaneous multistep forward prediction. Accompanying the GNN model is a sim-and-real co-training procedure that promotes generalization and robustness to noisy real data. This model is further integrated with a Model Predictive Path Integral (MPPI) controller for closed-loop navigation, which showcases the model’s speed and accuracy.
APA
Chen, N., III, W.R.J., Kramer-Bottiglio, R., Bekris, K. & Aanjaneya, M.. (2026). CableRobotGraphSim: A Graph Neural Network for Modeling Partially Observable Cable-Driven Robot Dynamics. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:169-182 Available from https://proceedings.mlr.press/v331/chen26a.html.

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