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From Geometries to Contact Graphs

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12397)


When a robot perceives its environment, it is not only important to know what kind of objects are present in it, but also how they relate to each other. For example in a cleanup task in a cluttered environment, a sensible strategy is to pick the objects with the least contacts to other objects first, to minimize the chance of unwanted movements not related to the current picking action. Estimating object contacts in cluttered scenes only based on passive observation is a complex problem. To tackle this problem, we present a deep neural network that learns physically stable object relations directly from geometric features. The learned relations are encoded as contact graphs between the objects. To facilitate training of the network, we generated a rich, publicly available dataset consisting of more than 25000 unique contact scenes, by utilizing a physics simulation. Different deep architectures have been evaluated and the final architecture, which shows good results in reconstructing contact graphs, is evaluated quantitatively and qualitatively.


  • Physical reasoning
  • Graph generation
  • Robotics

The research reported in this paper has been supported by the German Research Foundation DFG, as part of Collaborative Research Center 1320 “EASE - Everyday Activity Science and Engineering”. The research was conducted in subproject R05.

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Meier, M., Haschke, R., Ritter, H.J. (2020). From Geometries to Contact Graphs. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12397. Springer, Cham.

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  • Print ISBN: 978-3-030-61615-1

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