Abstract
We present a Transfer and Continual Learning method for robotic grasping tasks, based on small vision-depth (RGBD) datasets and realized through the use of Grasping Features. Given a network architecture composed by a CNN (Convolutional Neural Network) followed by a FCC (Fully Connected Cascade Neural Network), we exploit high-level features specific of the grasping tasks, as extracted by the convolutional network from RGBD images. These features are more descriptive of a grasping task than just visual ones, and thus more efficient for transfer learning purposes. Being datasets for visual grasping less common than those for image recognition, we also propose an efficient way to generate these data using only simple geometric structures. This reduces the computational burden of the FCC and allows to obtain a better performance with the same amount of data. Simulation results using the collaborative UR-10 robot and a jaw gripper are reported to show the quality of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bohg, J., Morales, A., Asfour, T., Kragic, D.: Data-driven grasp synthesis-a survey. IEEE Trans. Robot. 30(2), 289–309 (2014). https://doi.org/10.1109/TRO.2013.2289018
Delange, M., et al.: A continual learning survey: defying forgetting in classification tasks. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3366–3385 (2022). https://doi.org/10.1109/TPAMI.2021.3057446
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
Huh, M., Agrawal, P., Efros, A.A.: What makes ImageNet good for transfer learning? CoRR abs/1608.08614 (2016). http://arxiv.org/abs/1608.08614
Huszár, F.: Note on the quadratic penalties in elastic weight consolidation. Proc. Natl. Acad. Sci. 115(11), E2496–E2497 (2018). https://doi.org/10.1073/pnas.1717042115. https://www.pnas.org/content/115/11/E2496
Jiang, Y., Moseson, S., Saxena, A.: Efficient grasping from RGBD images: learning using a new rectangle representation. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 3304–3311 (2011). https://doi.org/10.1109/ICRA.2011.5980145
Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. 114(13), 3521–3526 (2017). https://doi.org/10.1073/pnas.1611835114. https://www.pnas.org/content/114/13/3521
Kragic, D., Daniilidis, K.: 3-D vision for navigation and grasping. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 811–824. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32552-1_32
Kuffner, J., Xiao, J.: Motion for manipulation tasks. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 897–930. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32552-1_36
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., Díaz-Rodríguez, N.: Continual learning for robotics: definition, framework, learning strategies, opportunities and challenges. Inf. Fusion 58, 52–68 (2020). https://doi.org/10.1016/j.inffus.2019.12.004. https://www.sciencedirect.com/science/article/pii/S1566253519307377
Monorchio, L., Evangelista, D., Imperoli, M., Pretto, A.: Learning from successes and failures to grasp objects with a vacuum gripper. In: IEEE/RSJ IROS Workshop on Task-Informed Grasping for Rigid and Deformable Object Manipulation (2018)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010). https://doi.org/10.1109/TKDE.2009.191
Quillen, D., Jang, E., Nachum, O., Finn, C., Ibarz, J., Levine, S.: Deep reinforcement learning for vision-based robotic grasping: a simulated comparative evaluation of off-policy methods. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 6284–6291 (2018). https://doi.org/10.1109/ICRA.2018.8461039
Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 512–519 (2014)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016). https://doi.org/10.1109/CVPR.2016.91
Saxena, A., Driemeyer, J., Ng, A.Y.: Robotic grasping of novel objects using vision. Int. J. Robot. Res. 27(2), 157–173 (2008). https://doi.org/10.1177/0278364907087172
Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., et al.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013). https://doi.org/10.1007/s11263-013-0620-5
Yen-Chen, L., Zeng, A., Song, S., Isola, P., Lin, T.Y.: Learning to see before learning to act: visual pre-training for manipulation. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 7286–7293 (2020). https://doi.org/10.1109/ICRA40945.2020.9197331
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. CoRR abs/1311.2901 (2013). http://arxiv.org/abs/1311.2901
Zhang, Q., Wu, Y.N., Zhu, S.C.: Interpretable convolutional neural networks. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8827–8836 (2018). https://doi.org/10.1109/CVPR.2018.00920
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Monorchio, L., Capotondi, M., Corsanici, M., Villa, W., De Luca, A., Puja, F. (2022). Transfer and Continual Supervised Learning for Robotic Grasping Through Grasping Features. In: Cuzzolin, F., Cannons, K., Lomonaco, V. (eds) Continual Semi-Supervised Learning. CSSL 2021. Lecture Notes in Computer Science(), vol 13418. Springer, Cham. https://doi.org/10.1007/978-3-031-17587-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-17587-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17586-2
Online ISBN: 978-3-031-17587-9
eBook Packages: Computer ScienceComputer Science (R0)