I completed my PhD work on data-efficient simulation-to-reality transfer at the Robotics, Perception and Learning lab in KTH, Stockholm, working in the group headed by Danica Kragic. My thesis was on "Transfer-Aware Kernels, Priors and Latent Spaces from Simulation to Real Robots".
Previously, I was a Masters student at the Robotics Institute at Carnegie Mellon University, developing Bayesian optimization approaches for learning control parameters for bipedal locomotion (with Akshara Rai and Chris Atkeson). During my time at CMU my advisor was Emma Brunskill and in her group I worked on developing Reinforcement Learning algorithms for education.
A few years earlier, I was a software engineer at Google, first in the Search Personalization group and then in the Character Recognition team (developing open-source OCR engine Tesseract).
Publications, talks, preprints:
R. Antonova *, P. Shi*, H. Yin, Z. Weng, Danica Kragic Jensfelt, D. Kragic. Dynamic Environments with Deformable Objects. To appear in NeurIPS (Datasets and Benchmarks Track), 2021.
J Yang, J Zhang, C Settle, A Rai, R Antonova, J Bohg. Learning Periodic Tasks from Human Demonstrations. arXiv:2109.14078, 2021.
R. Antonova *, A. Varava *, P. Shi, J. Carvalho, D. Kragic. Sequential Topological Representations for Predictive Models of Deformable Objects. In Proceedings of Machine Learning Research as part of the conference on Learning for Dynamics and Control (L4DC), 2021.
R. Antonova, M. Maydanskiy, D. Kragic, S. Devlin, K. Hofmann. Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control. [arXiv]
M. Hwasser, D. Kragic, R. Antonova. Variational Auto-Regularized Alignment for Sim-to-Real Control. IEEE International Conference on Robotics and Automation (ICRA), 2020.
|ICRA2020 Talk||ICRA2020 Experiments Video|
R. Antonova *, A. Rai *, T. Li, D. Kragic. Bayesian Optimization in Variational Latent Spaces with Dynamic Compression. Conference on Robot Learning (CoRL), 2019. [arXiv]
|CoRL2019 Talk||CoRL2019 Experiments Video|
R. Antonova *, M. Kokic *, JA Stork, D. Kragic. Global Search with Bernoulli Alternation Kernel for Task-Oriented Grasping Informed by Simulation. Conference on Robot Learning (CoRL), PMLR 87: 641-650, 2018. [arXiv]
|CoRL2018 Talk||CoRL2018 Experiments Video|
A. Rai *, R. Antonova *, F. Meier, C. Atkeson. Using Simulation to Improve Sample Efficiency of Bayesian Optimization for Bipedal Robots. Journal of Machine Learning Research (JMLR) Bayesian Optimization Issue, (49): 1-24, 2019.
A. Rai *, R. Antonova *, S. Song, W. Martin, H. Geyer, C. Atkeson. Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped. IEEE International Conference on Robotics and Automation (ICRA), 2018 [arXiv, poster]
R. Antonova *, A. Rai *, C. Atkeson. Deep Kernels for Optimizing Locomotion Controllers . 1st Conference on Robot Learning (CoRL), PMLR 78: 47-56, 2017. [arXiv, poster]
R. Antonova *, A. Rai *, C. Atkeson. Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion . IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), 2016. [arXiv]
|ICRA2018 Talk||CoRL2017 Talk|
R. Antonova *, S. Cruciani *. Unlocking the Potential of Simulators: Design with RL in Mind. Presented at the 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2017. [arXiv, poster]
R. Antonova. Bayesian Optimization for Policy Search: Case Studies in Robotics and Education . Masters thesis. Robotics Institute, Carnegie Mellon University, 2016.
R. Antonova, J. Runde, M. H. Lee, E. Brunskill. Automatically Learning to Teach to the Learning Objectives. In Proceedings of ACM Conference on Learning @ Scale, 2016. [poster]
R. Antonova, J. Runde, C. Dann, E. Brunskill. Improving the Sample Efficiency of Bayesian Optimization Policy Search for Optimal Stopping Problems. Workshop on Data-Efficient Machine Learning at ICML, 2016.
R. Antonova. Multi-task Value of Information Planning for Sequential Multi-task Bandits. Technical Report. Robotics Institute, Carnegie Mellon University, 2016.
Carlo Rapisarda. Reinforcement Learning for Dexterity Transfer Between Manipulators. KTH MS Thesis, June 2019
Martin Hwasser. Converting Deterministic Simulators to Realistic Stochastic Models via Data Alignment. KTH MS Thesis, June 2019