SPONGE: Sequence Planning with Deformable-ON-Rigid Contact Prediction from Geometric Features

Tran Nguyen Le*, Fares Abu-Dakka, Ville Kyrki

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaKonferenssiesitysScientificvertaisarvioitu


Planning robotic manipulation tasks, especially those that involve interaction between deformable and rigid objects, is challenging due to the complexity in predicting such interactions. We introduce SPONGE, a sequence planning pipeline powered by a deep learning-based contact prediction model for contacts between deformable and rigid bodies under interactions. The contact prediction model is trained on synthetic data generated by a developed simulation environment to learn the mapping from point-cloud observation of a rigid target object and the pose of a deformable tool, to 3D representation of the contact points between the two bodies. We experimentally evaluated the proposed approach for a dish cleaning task both in simulation and on a real \panda with real-world objects. The experimental results demonstrate that in both scenarios the proposed planning pipeline is capable of generating high-quality trajectories that can accomplish the task by achieving more than 90\% area coverage on different objects of varying sizes and curvatures while minimizing travel distance. Code and video are available at: \url{this https URL}.
TilaHyväksytty/In press - 7 toukok. 2023
OKM-julkaisutyyppiEi oikeutettu
TapahtumaWorkshop on Representing and Manipulating Deformable Objects - ExCeL London, London, Iso-Britannia
Kesto: 29 toukok. 202329 toukok. 2023


WorkshopWorkshop on Representing and Manipulating Deformable Objects


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