Manipulation of granular materials by learning particle interactions

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

1 Sitaatiot (Scopus)
14 Lataukset (Pure)

Abstrakti

Manipulation of granular materials such as sand or rice remains an unsolved problem due to challenges such as the difficulty of defining their configuration or modeling the materials and their particles interactions. Current approaches tend to simplify the material dynamics and omit the interactions between the particles. In this paper, we propose to use a graph-based representation to model the interaction dynamics of the material and rigid bodies manipulating it. This allows the planning of manipulation trajectories to reach a desired configuration of the material. We use a graph neural network (GNN) to model the particle interactions via message-passing. To plan manipulation trajectories, we propose to minimise the Wasserstein distance between a predicted distribution of granular particles and their desired configuration. We demonstrate that the proposed method is able to pour granular materials into the desired configuration both in simulated and real scenarios.

AlkuperäiskieliEnglanti
Artikkeli2
Sivut5663-5670
JulkaisuIEEE Robotics and Automation Letters
Vuosikerta7
Numero2
DOI - pysyväislinkit
TilaJulkaistu - huhtik. 2022
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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