Abstract
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.
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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Notes
- 1.
Dataset is available at https://pub.uni-bielefeld.de/record/2943056.
References
Anders, A.S., Kaelbling, L.P., Lozano-Perez, T.: Reliably arranging objects in uncertain domains. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1603–1610. IEEE (2018)
Battaglia, P., et al.: Interaction networks for learning about objects, relations and physics. In: Advances in neural information processing systems, pp. 4502–4510 (2016)
Chawla, N.V.: Data mining for imbalanced datasets: an overview. In: Maimon O., Rokach L. (eds) Data Mining and Knowledge Discovery Handbook. pp. 875–886. Springer, Boston, MA (2009). https://doi.org/10.1007/978-0-387-09823-4_45
Dreher, C.R., Wächter, M., Asfour, T.: Learning object-action relations from bimanual human demonstration using graph networks. IEEE Robot. Autom. Lett. 5(1), 187–194 (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 3, pp. 2149–2154. IEEE (2004)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980–2988 (2017)
Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)
Najafi, E., Shah, A., Lopes, G.A.: Robot contact language for manipulation planning. IEEE/ASME Trans. Mechatron. 23(3), 1171–1181 (2018)
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in neural information processing systems, pp. 5099–5108 (2017)
Rashid, M., Kjellstrom, H., Lee, Y.J.: Action graphs: weakly-supervised action localization with graph convolution networks. In: The IEEE Winter Conference on Applications of Computer Vision, pp. 615–624 (2020)
Rosman, B., Ramamoorthy, S.: Learning spatial relationships between objects. Int. J. Robot. Res. 30(11), 1328–1342 (2011)
Sanchez-Gonzalez, A., et al.: Graph networks as learnable physics engines for inference and control. In: International Conference on Machine Learning, pp. 4470–4479 (2018)
Scherzinger, S., Roennau, A., Dillmann, R.: Contact skill imitation learning for robot-independent assembly programming. In: IEEE International Conference on Intelligent Robots and Systems, pp. 4309–4316. IEEE (2019)
Smith, S.L., Kindermans, P.J., Ying, C., Le, Q.V.: Don’t decay the learning rate, increase the batch size. arXiv preprint arXiv:1711.00489 (2017)
Wu, J., Wang, L., Wang, L., Guo, J., Wu, G.: Learning actor relation graphs for group activity recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9964–9974. IEEE (2019)
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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. https://doi.org/10.1007/978-3-030-61616-8_44
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