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[12]

R. Laezza, Robot learning for deformable object manipulation tasks. PhD Thesis: Chalmers University of Technology, 2024. Available: https://research.chalmers.se/publication/540359/file/540359_Fulltext.pdf

[11]

K. Freitag, R. Laezza, J. Zbinden, and M. Ortiz-Catalan, “Improving bionic limb control through reinforcement learning in an interactive game environment,” ICML Workshop, 2023, Available: https://interactive-learning-implicit-feedback.github.io/

[10]

R. Laezza, M. Shetab-Bushehri, E. Ozgür, Y. Mezouar, and Y. Karayiannidis, “Offline reinforcement learning for shape control of deformable linear objects from limited real data,” ICRA Workshop, 2023, Available: https://deformable-workshop.github.io/icra2023/

[9]

F. Süberkrüb, R. Laezza, and Y. Karayiannidis, “Feel the tension: Manipulation of deformable linear objects in environments with fixtures using force information,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 11216–11222. doi: 10.1109/IROS47612.2022.9982065.

[8]

G. A. Waltersson, R. Laezza, and Y. Karayiannidis, “Planning and control for cable-routing with dual-arm robot,” in 2022 IEEE International Conference on Robotics and Automation (ICRA), 2022, pp. 1046–1052. doi: 10.1109/ICRA46639.2022.9811765.

[7]

R. Laezza, F. Süberkrüb, and Y. Karayiannidis, “Blind manipulation of deformable linear objects based on force information from environmental contacts,” ICRA Workshop, 2022, Available: https://deformable-workshop.github.io/icra2022/

[6]

R. Laezza, Robot learning for manipulation of deformable linear objects. Lic. Thesis: Chalmers University of Technology, 2021. Available: https://research.chalmers.se/publication/526364/file/526364_Fulltext.pdf

[5]

R. Laezza and Y. Karayiannidis, “Learning shape control of elastoplastic deformable linear objects,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 4438–4444. doi: 10.1109/ICRA48506.2021.9561984.

[4]

R. Laezza, R. Gieselmann, F. T. Pokorny, and Y. Karayiannidis, “ReForm: A robot learning sandbox for deformable linear object manipulation,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 4717–4723. doi: 10.1109/ICRA48506.2021.9561766.

[3]

R. Laezza, R. Gieselmann, F. T. Pokorny, and Y. Karayiannidis, “Presenting ReForm, a robot learning sandbox for deformable linear object manipulation,” ICRA Workshop, 2021, Available: https://deformable-workshop.github.io/icra2021/

[2]

R. Laezza and Y. Karayiannidis, “Shape control of elastoplastic deformable linear objects through reinforcement learning,” IROS Workshop, 2020, Available: http://commandia.unizar.es/irosworkshop2020/

[1]

R. Laezza, Deep neural networks for myoelectric pattern recognition - an implementation for multifunctional control. MSc. Thesis: Chalmers University of Technology, 2018. Available: https://odr.chalmers.se/bitstream/20.500.12380/254980/1/254980.pdf