| Latent space roadmap for visual action planning of deformable and rigid object manipulation M Lippi, P Poklukar, MC Welle, A Varava, H Yin, A Marino, D Kragic 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 38 | 2020 |
| Data-efficient visuomotor policy training using reinforcement learning and generative models A Ghadirzadeh, P Poklukar, V Kyrki, D Kragic, M Björkman arXiv preprint arXiv:2007.13134, 2020 | 9 | 2020 |
| Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms A Ghadirzadeh, X Chen, P Poklukar, C Finn, M Björkman, D Kragic 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 7 | 2021 |
| Enabling visual action planning for object manipulation through latent space roadmap M Lippi, P Poklukar, MC Welle, A Varava, H Yin, A Marino, D Kragic IEEE Transactions on Robotics, 2022 | 4 | 2022 |
| GeomCA: Geometric Evaluation of Data Representations P Poklukar, A Varava, D Kragic Proceedings of the 38th International Conference on Machine Learning 139 …, 2021 | 4 | 2021 |
| Modeling assumptions and evaluation schemes: On the assessment of deep latent variable models. J Bütepage, P Poklukar, D Kragic CVPR Workshops, 9-12, 2019 | 4 | 2019 |
| Delaunay Component Analysis for Evaluation of Data Representations DK Petra Poklukar, Vladislav Polianskii, Anastasia Varava, Florian Pokorny International Conference on Learning Representations, 2022 | 3* | 2022 |
| GraphDCA--a Framework for Node Distribution Comparison in Real and Synthetic Graphs C Ceylan, P Poklukar, H Hultin, A Kravchenko, A Varava, D Kragic arXiv preprint arXiv:2202.03884, 2022 | 2 | 2022 |
| Batch Curation for Unsupervised Contrastive Representation Learning MC Welle, P Poklukar, D Kragic arXiv preprint arXiv:2108.08643, 2021 | 2 | 2021 |
| Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models A Ghadirzadeh, P Poklukar, K Arndt, C Finn, V Kyrki, D Kragic, ... arXiv preprint arXiv:2204.08573, 2022 | 1 | 2022 |
| GMC--Geometric Multimodal Contrastive Representation Learning P Poklukar, M Vasco, H Yin, FS Melo, A Paiva, D Kragic arXiv preprint arXiv:2202.03390, 2022 | 1 | 2022 |
| Few-Shot Learning with Weak Supervision A Ghadirzadeh, P Poklukar, X Chen, H Yao, H Azizpour, M Björkman, ... Learning to Learn-Workshop at ICLR 2021, 2021 | 1 | 2021 |
| Augment-Connect-Explore: a Paradigm for Visual Action Planning with Data Scarcity M Lippi, MC Welle, P Poklukar, A Marino, D Kragic arXiv preprint arXiv:2203.13034, 2022 | | 2022 |
| Learning and Evaluating the Geometric Structure of Representation Spaces P Poklukar KTH Royal Institute of Technology, 2022 | | 2022 |
| GraphDCA - a Framework for Node Distribution Comparison in Real and Synthetic Graphs P Poklukar, C Ceylan, H Hultin, O Kravchenko, A Varava, D Kragic | | 2022 |
| Seeing the whole picture instead of a single point: Self-supervised likelihood learning for deep generative models P Poklukar, J Bütepage, D Kragic | | 2019 |
| On the real spectrum compactification of Teichmüller space: master thesis P Poklukar Univerza v Ljubljani, Fakulteta za matematiko in fiziko, 2016 | | 2016 |
| Operacije na gladkih mnogoterostih: delo diplomskega seminarja P Poklukar Univerza v Ljubljani, Fakulteta za matematiko in fiziko, 2014 | | 2014 |
| Latent Space Roadmap for Visual Action Planning M Lippi, P Poklukar, MC Welle, A Varava, H Yin, A Marino, D Kragic | | |
| Analyzing Representations through Interventions P Poklukar, MC Welle, A Varava, D Kragic | | |
Danica KragicProfessor of Computer Science, KTH - Royal Institute of TechnologyVerified email at kth.se
