Robotic perception
Robotic perception
Robotic perception

Publications

  1. Rafał Staszak, Bartłomiej Kulecki, Witold Sempruch, Dominik Belter, What’s on the Other Side? a Single-View 3D Scene Reconstruction, Proceedings of the 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), December 11-13, 2022, Singapore, s. 173-180, 2022
  2. Dominik Belter, Informed Guided Rapidly-Exploring Random Trees*-Connect for Path Planning of Walking Robots, Proceedings of the 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), December 11-13, 2022, Singapore, s. 709-714, 2022
  3. Kamil Młodzikowski, Dominik Belter, CNN-based Joint State Estimation During Robotic Interaction with Articulated Objects, Proceedings of the 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), December 11-13, 2022, Singapore, s. 78-83, 2022
  4. Rafał Staszak, Dominik Belter, 3D Object Localization with 2D CNN Object Detector and 2D Localization, Proceedings of the 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), December 11-13, 2022, Singapore, s. 715-720, 2022
  5. Kamil Młodzikowski, Dominik Belter, CNNs for State Estimation of Articulated Objects, Proceedings of the 3rd Polish Conference on Artificial Intelligence, April 25-27, 2022, Gdynia, Poland, s. 112-115, 2022
  6. Rafal Staszak, Piotr Michałek, Jakub Chudziński, Marek Kopicki, Dominik Belter, 3D Reconstruction of Non-Visible Surfaces of Objects from a Single Depth View – Comparative Study, Wojciechowski A.(Ed.), Lipiński P.(Ed.)., Progress in Polish Artificial Intelligence Research 4, Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, ISBN 978-83-66741-92-8, pp. 19-24, 2023
  7. Mikołaj Zieliński, Dominik Belter, On the importance of the RGB-D sensor model in the CNN-based robotic perception, Wojciechowski A.(Ed.), Lipiński P.(Ed.)., Progress in Polish Artificial Intelligence Research 4, Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, ISBN 978-83-66741-92-8, pp. 495-499, 2023 (Best Paper Award of Robotics and Autonomous Systems Session)
  8. Kamil Młodzikowski, Dominik Belter, Mixing Synthetic and Real-world Datasets Strategy for Improved Generalization of the CNN, Wojciechowski A.(Ed.), Lipiński P.(Ed.)., Progress in Polish Artificial Intelligence Research 4, Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, ISBN 978-83-66741-92-8, pp. 435-441, 2023
  9. Adam Krawczyk, Jakub Marciniak, Dominik Belter, Comparison of machine learning techniques for self-collisions checking of manipulating robots, 2023 27th International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 472-477, 2023
  10. Mikołaj Zieliński, Bartłomiej Kulecki, Dominik Belter, Direct Object Reconstruction on RGB-D Images in Cluttered Environment, 2023 European Conference on Mobile Robots (ECMR), pp. 1-7, 2023
  11. Arkadiusz Mula, Kamil Młodzikowski, Patryk Gawron, Dominik Belter, Neural-Based Detection and Segmentation of Articulated Objects for Robotic Interaction in a Household Environment, Applied Sciences, Vol. 13(5), pp. 1-18, 2023
  12. Rafał Staszak, Dominik Belter, 3D object reconstruction: A comprehensive view-dependent dataset, Data in Brief, Vol. 55, 110569, 2024, https://doi.org/10.1016/j.dib.2024.110569
  13. Bartłomiej Kulecki and Dominik Belter, Boosting Machine Learning Techniques with Positional Encoding for Robot Collision Checking, 2024 13th International Workshop on Robot Motion and Control (RoMoCo), Poznań, Poland, 2024, pp. 90-95, 2024
  14. Bartłomiej Kulecki, Dominik Belter, Positional Encoding for Robot Neural Self-Collision Checking, 2024 (in print)
  15. Rafał Staszak, Bartłomiej Kulecki, Marek Kraft, Dominik Belter, MirrorNet: Hallucinating 2.5D Depth Images for Efficient 3D Scene Reconstruction, 2024 (under review)
  16. Simone Tolomei, Dominik Belter, Jakub Bednarek, Franco Angelini, Manolo Garabini, Learning-based Foot-Shape-Aware Foothold Selection for Quadrupedal Robots, 2024 (under review)

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