Manipulating deformable linear objects (DLOs) is a challenging task for a robotic system due to their unpredictable configuration, high-dimensional state space and complex nonlinear dynamics. This letter presents a framework addressing the manipulation of DLOs, specifically targeting the model-based shape control task with the simultaneous online gradient-based estimation of model parameters. In the proposed framework, a neural network is trained to mimic the DLO dynamics using the data generated with an analytical DLO model for a broad spectrum of its parameters. The neural network-based DLO model is conditioned on these parameters and employed in an online phase to perform the shape control task by estimating the optimal manipulative action through a gradient-based procedure. In parallel, gradient-based optimization is used to adapt the DLO model parameters to make the neural network-based model better capture the dynamics of the real-world DLO being manipulated and match the observed deformations. To assess its effectiveness, the framework is tested across a variety of DLOs, surfaces, and target shapes in a series of experiments. The results of these experiments demonstrate the validity and efficiency of the proposed methodology compared to existing methods.
Deformable Linear Objects Manipulation With Online Model Parameters Estimation / Caporali, Alessio; Kicki, Piotr; Galassi, Kevin; Zanella, Riccardo; Walas, Krzysztof; Palli, Gianluca. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 9:3(2024), pp. 2598-2605. [10.1109/LRA.2024.3357310]
Deformable Linear Objects Manipulation With Online Model Parameters Estimation
Kevin Galassi;
2024
Abstract
Manipulating deformable linear objects (DLOs) is a challenging task for a robotic system due to their unpredictable configuration, high-dimensional state space and complex nonlinear dynamics. This letter presents a framework addressing the manipulation of DLOs, specifically targeting the model-based shape control task with the simultaneous online gradient-based estimation of model parameters. In the proposed framework, a neural network is trained to mimic the DLO dynamics using the data generated with an analytical DLO model for a broad spectrum of its parameters. The neural network-based DLO model is conditioned on these parameters and employed in an online phase to perform the shape control task by estimating the optimal manipulative action through a gradient-based procedure. In parallel, gradient-based optimization is used to adapt the DLO model parameters to make the neural network-based model better capture the dynamics of the real-world DLO being manipulated and match the observed deformations. To assess its effectiveness, the framework is tested across a variety of DLOs, surfaces, and target shapes in a series of experiments. The results of these experiments demonstrate the validity and efficiency of the proposed methodology compared to existing methods.File | Dimensione | Formato | |
---|---|---|---|
Deformable_Linear_Objects_Manipulation_With_Online_Model_Parameters_Estimation.pdf
accesso aperto
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Creative commons
Dimensione
1.28 MB
Formato
Adobe PDF
|
1.28 MB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2991570