Abstract
Robotic manipulation of deformable objects is challenging due to the great variety of materials and shapes. This task is even more complex when the object is also fragile, and the allowed amount of deformation needs to be constrained. For the goal of driving a thin fragile deformable object to a target 2D position and orientation, we propose a manipulation method based on executing planar pushing actions on the object edges with a robotic arm. Firstly, we obtain a probabilistic model through Gaussian process regression, which represents the time-varying deformation properties of the system. Then, we exploit the model in the framework of an Adaptive Bayesian Optimization (ABO) algorithm to compute the pushing action at each instant. We evaluate our proposal in simulation.
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Acknowledgments
This work has received funding from the European Union’s Horizon Europe research and innovation program under grant agreement No. 101070600, project SoftEnable. This work was also supported via projects REMAIN S1/1.1/E0111 (Interreg Sudoe Programme, ERDF), PID 2021-124137OB-I00 and TED2021-130224B-I00 (funded by MCIN/AEI/10.13039/501100011033), projects T45_23R and T73_23R by Gobierno de Aragón, ERDF A way of making Europe and the European Union NextGenerationEU/PRTR. The first author was partially supported by the EU through the European Social Fund (ESF) “Construyendo Europa desde Aragón”.
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Herguedas, R., Sundaram, A.M., López-Nicolás, G., Roa, M.A., Sagüés, C. (2024). Adaptive Bayesian Optimization for Robotic Pushing of Thin Fragile Deformable Objects. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-031-58676-7_28
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DOI: https://doi.org/10.1007/978-3-031-58676-7_28
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