Dealing with Ambiguity in Robotic Grasping via Multiple Predictions

Ghazaei G, Laina I, Rupprecht C, Tombari F, Navab N, Nazarpour K (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 11364 LNCS

Pages Range: 38-55

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Perth, WA, AUS

ISBN: 9783030208691

DOI: 10.1007/978-3-030-20870-7_3

Abstract

Humans excel in grasping and manipulating objects because of their life-long experience and knowledge about the 3D shape and weight distribution of objects. However, the lack of such intuition in robots makes robotic grasping an exceptionally challenging task. There are often several equally viable options of grasping an object. However, this ambiguity is not modeled in conventional systems that estimate a single, optimal grasp position. We propose to tackle this problem by simultaneously estimating multiple grasp poses from a single RGB image of the target object. Further, we reformulate the problem of robotic grasping by replacing conventional grasp rectangles with grasp belief maps, which hold more precise location information than a rectangle and account for the uncertainty inherent to the task. We augment a fully convolutional neural network with a multiple hypothesis prediction model that predicts a set of grasp hypotheses in under 60 ms, which is critical for real-time robotic applications. The grasp detection accuracy reaches over $$90\%$$ for unseen objects, outperforming the current state of the art on this task.

Involved external institutions

Newcastle University GB United Kingdom (GB)

How to cite

APA:

Ghazaei, G., Laina, I., Rupprecht, C., Tombari, F., Navab, N., & Nazarpour, K. (2019). Dealing with Ambiguity in Robotic Grasping via Multiple Predictions. In C.V. Jawahar, Konrad Schindler, Greg Mori, Hongdong Li (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 38-55). Perth, WA, AUS: Springer Verlag.

MLA:

Ghazaei, Ghazal, et al. "Dealing with Ambiguity in Robotic Grasping via Multiple Predictions." Proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, Perth, WA, AUS Ed. C.V. Jawahar, Konrad Schindler, Greg Mori, Hongdong Li, Springer Verlag, 2019. 38-55.

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