Open Access
| Issue |
EPJ Web Conf.
Volume 367, 2026
Fifth International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2026)
|
|
|---|---|---|
| Article Number | 02007 | |
| Number of page(s) | 17 | |
| Section | Intelligent Automation | |
| DOI | https://doi.org/10.1051/epjconf/202636702007 | |
| Published online | 29 April 2026 | |
- Tong Li, Yuhang Yan, Chengshun Yu, Jing An, Yifan Wang, Gang Chen. A comprehensive review of robot intelligent grasping based on tactile perception, Robotics and Computer-Integrated Manufacturing, 90, 102792. (2024) https://doi.org/10.1016/j.rcim.2024.102792. [Google Scholar]
- Levine, S., Kumar, A., Tucker, G., & Fu, J. Offline reinforcement learning for robotic manipulation. The International Journal of Robotics Research, 41(7), 835–857. (2022). https://doi.org/10.1177/02783649211048370 [Google Scholar]
- Li, J., Vinel, A., & Mak, T. Deep learning-based vision system for automated waste sorting. Robotics and Computer-Integrated Manufacturing, 70, 102066. (2021). https://doi.org/10.1016/j.rcim.2021.102066 [Google Scholar]
- Zhao, R., Zhang, K., & Wang, Y. Vision-guided robotic system for solid waste sorting using deep learning. IEEE Access, 8, 160294–160304. (2020). https://doi.org/10.1109/ACCESS.2020.3016460 [Google Scholar]
- Kragic, D., & Bjorkman, M. Vision-guided robotic grasping and manipulation. Journal of Robotics Research, 25(7), 951–967 (2006). [Google Scholar]
- Levine, S., Pastor, P., Krizhevsky, A., & Quillen, D. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data. The International Journal of Robotics Research, 37(4–5), 421–436. (2018). https://doi.org/10.1177/0278364917710318 [Google Scholar]
- Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., … Levine, S. QT-Opt: Scalable deep reinforcement learning for vision-based robotic manipulation. (2018). arXiv preprint arXiv:1806.10293. https://doi.org/10.48550/arXiv.1806.10293 [Google Scholar]
- Lahoti J, Sn J, Krishna MV, Prasad M, Bs R, Mysore N, Nayak JS. Multi-class waste segregation using computer vision and robotic arm. PeerJ Comput Sci. 10:e1957. (2024) https://doi.org/10.7717/peerj-cs.1957. [Google Scholar]
- Li J, Vinel A, Mak TM. Deep learning-based vision system for automated waste sorting. Robot Comput Integr Manuf. 70:102066. (2021) https://doi.org/10.1016/j.rcim.2021.102066 [Google Scholar]
- Chahine K, Ghazal M, Bazzi S, Hokayem P. Automatic sorting of solid wastes using sensor fusion. Int J Eng Technol. 9 (6):4413–4419. (2017) [Google Scholar]
- Barrionuevo AM, Yanes L, Gutiérrez R, Toral ML. Optimizing plastic waste collection in water bodies using heterogeneous autonomous surface vehicles with deep reinforcement learning [preprint]. arXiv 2024 Dec 10. Available from: https://arxiv.org/abs/2412.02316 [Google Scholar]
- Nguyen VP, Bohra Dhyan S, Mai V, Han BS, Chow WT. Bioinspiration and biomimetic art in robotic grippers. Micromachines (Basel). 14(9):1772. (2023) https://doi.org/10.3390/mi14091772 [Google Scholar]
- Saxena A, Driemeyer J, Ng AY. Robotic Grasping of Novel Objects using Vision. The International Journal of Robotics Research. 27(2):157–173. (2008) https://doi.org/10.1177/0278364907087172 [Google Scholar]
- Wang, S., Nikolić, M.N., Lam, T.L., Gao, Q., Ding, R. and Zhang, T. Robot Manipulation Based on Embodied Visual Perception: A Survey. CAAI Trans. Intell. Technol, 10: 945–958. (2025) https://doi.org/10.1049/cit2.70022 [Google Scholar]
- Zhou R, Fazel M, Du S. Toward a theoretical foundation of policy optimization for learning control policies. Annu Rev Control Robot Auton Syst. 2023 6:325–55. (2023) https://doi.org/10.1146/annurev-control-061522-085734. [Google Scholar]
- Bohg, J., Morales, A., Asfour, T., & Kragic, D. Data-driven grasp synthesis—a survey. IEEE Transactions on Robotics, 30(2), 289–309. (2014). https://doi.org/10.1109/TRO.2013.2289018 [Google Scholar]
- Mordatch I, Lowrey K, Todorov E. Learning to grasp with deep neural networks and haptic perception. In: Proceedings of the Robotics: Science and Systems (RSS); Jun 18-22. Rome, Italy. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.