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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Asif, U., Bennamoun, M., Sohel, F.A.: RGB-D object recognition and grasp detection using hierarchical cascaded forests. IEEE Trans. Rob. 33(3), 547–564 (2017)
Belagiannis, V., Zisserman, A.: Recurrent human pose estimation. In: International Conference on Automatic Face & Gesture Recognition (FG 2017) (2017)
Bicchi, A., Kumar, V.: Robotic grasping and contact: a review. In: Proceedings of 2000 IEEE International Conference on Robotics and Automation (ICRA), vol. 1, pp. 348–353. IEEE (2000)
Bulat, A., Tzimiropoulos, G.: Human pose estimation via convolutional part heatmap regression. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 717–732. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_44
Bulat, A., Tzimiropoulos, G.: Super-fan: integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B (Methodol.) (1977)
Du, X., et al.: Articulated multi-instrument 2D pose estimation using fully convolutional networks. IEEE Trans. Med. Imaging (2018)
Guo, D., Sun, F., Liu, H., Kong, T., Fang, B., Xi, N.: A hybrid deep architecture for robotic grasp detection. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2017)
Guzman-Rivera, A., et al.: Multi-output learning for camera relocalization. In: Conference on Computer Vision and Pattern Recognition (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Jiang, Y., Moseson, S., Saxena, A.: Efficient grasping from RGB-D images: learning using a new rectangle representation. In: International Conference on Robotics and Automation (ICRA). IEEE (2011)
Kehoe, B., Patil, S., Abbeel, P., Goldberg, K.: A survey of research on cloud robotics and automation
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
Kumra, S., Kanan, C.: Robotic grasp detection using deep convolutional neural networks. arXiv preprint arXiv:1611.08036 (2016)
Laina, I., et al.: Concurrent segmentation and localization for tracking of surgical instruments. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 664–672. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_75
Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., Navab, N.: Deeper depth prediction with fully convolutional residual networks. In: 2016 Fourth International Conference on 3D Vision (3DV). IEEE (2016)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE (1998)
Lee, S., Prakash, S.P.S., Cogswell, M., Ranjan, V., Crandall, D., Batra, D.: Stochastic multiple choice learning for training diverse deep ensembles. In: Advances in Neural Information Processing Systems (2016)
Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. Int. J. Rob. Res. (2015)
Levine, S., Pastor, P., Krizhevsky, A., Quillen, D.: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. arXiv preprint arXiv:1603.02199 (2016)
Li, Z., Chen, Q., Koltun, V.: Interactive image segmentation with latent diversity. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Mahler, J., et al.: Dex-net 2.0: deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics. arXiv preprint arXiv:1703.09312 (2017)
McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, Hoboken (2004)
Merget, D., Rock, M., Rigoll, G.: Robust facial landmark detection via a fully-convolutional local-global context network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Miller, A.T., Allen, P.K.: Graspit! a versatile simulator for robotic grasping. IEEE Rob. Autom. Mag. (2004)
Papandreou, G., et al.: Towards accurate multi-person pose estimation in the wild. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Redmon, J., Angelova, A.: Real-time grasp detection using convolutional neural networks. In: 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2015)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (2015)
Rochan, M., Ye, L., Wang, Y.: Video summarization using fully convolutional sequence networks. arXiv preprint arXiv:1805.10538 (2018)
Rupprecht, C., et al.: Learning in an uncertain world: representing ambiguity through multiple hypotheses. In: International Conference on Computer Vision (ICCV) (2017)
Saxena, A., Driemeyer, J., Ng, A.Y.: Robotic grasping of novel objects using vision. Int. J. Rob. Res. (2008)
Varley, J., DeChant, C., Richardson, A., Nair, A., Ruales, J., Allen, P.: Shape completion enabled robotic grasping. arXiv preprint arXiv:1609.08546 (2016)
Vedaldi, A., Lenc, K.: MatConvNet - convolutional neural networks for MATLAB. In: Proceeding of the ACM International Conference on Multimedia (2015)
Viereck, U., Pas, A., Saenko, K., Platt, R.: Learning a visuomotor controller for real world robotic grasping using simulated depth images. In: Conference on Robot Learning (2017)
Wang, Z., Li, Z., Wang, B., Liu, H.: Robot grasp detection using multimodal deep convolutional neural networks. Adv. Mech. Eng. (2016)
Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Zapata-Impata, B.S.: Using geometry to detect grasping points on 3D unknown point cloud. In: International Conference on Informatics in Control, Automation and Robotics (2017)
Acknowledgments
This work is supported by UK Engineering and Physical Sciences Research Council (EP/R004242/1). We also gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan Xp GPU used for the experiments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ghazaei, G., Laina, I., Rupprecht, C., Tombari, F., Navab, N., Nazarpour, K. (2019). Dealing with Ambiguity in Robotic Grasping via Multiple Predictions. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-20870-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20869-1
Online ISBN: 978-3-030-20870-7
eBook Packages: Computer ScienceComputer Science (R0)