Rika Antonova

A Brief Bio

Update: After a fruitful search for a faculty position in Machine Learning + Robotics, I am happy to share that I decided to join the University of Cambridge. If you are interested in working with me at Cambridge - please reach out to connect (PhD applications are due on December 5th for those seeking funding; for those with external funding and MPhil applicants - the due dates are later in Spring).

I am a postdoctoral scholar at Stanford University and a recipient of the NSF/CRA Computing Innovation Fellowship. Currently, I work at the Interactive Perception and Robot Learning Lab headed by Jeannette Bohg. I completed my PhD work on data-efficient simulation-to-reality transfer at KTH (Sweden) in the group headed by Danica Kragic. I also had the opportunity to intern at NVIDIA Robotics (Seattle, USA) and Microsoft Research (Cambridge, UK).

Previously, I obtained my Master's degree from the Robotics Institute at Carnegie Mellon University, where I developed Bayesian optimization methods 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 data-efficient reinforcement learning.

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 an open-source OCR engine Tesseract).

Recent CoRL, IROS, and ICRA 2023 presentations

J Yang, C Deng, J Wu, R Antonova, L Guibas, J Bohg. EquivAct: SIM(3)-Equivariant Visuomotor Policies beyond Rigid Object Manipulation. [arXiv] (under review). Short version presented at the "Out-of-Distribution Generalization in Robotics" workshop at the Conference on Robot Learning (CoRL), 2023.

M Li, R Antonova, D Sadigh, J Bohg. Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation. IEEE International Conference on Robotics and Automation (ICRA), 2023.

J Wu, R Antonova, A Kan, M Lepert, A Zeng, S Song, J Bohg, S Rusinkiewicz, T Funkhouser. TidyBot: Personalized Robot Assistance with Large Language Models. IEEE International Conference on Intelligent Robots and Systems (IROS), 2023.

C Pan, M Lepert, S Yuan, R Antonova, J Bohg. In-Hand Manipulation of Unknown Objects with Tactile Sensing for Insertion. IEEE International Conference on Intelligent Robots and Systems (IROS), 2023.

Publications, Talks, Preprints

R Antonova *, J Yang *, K Jatavallabhula, J Bohg. Rethinking Optimization with Differentiable Simulation from a Global Perspective. Conference on Robot Learning (CoRL), 2022. Selected for oral presentation (6.5% acceptance rate).

R. Antonova, J. Yang, P. Sundaresan, D. Fox, F. Ramos, J. Bohg. A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation. IEEE Robotics and Automation Letters (RA-L), 2022. [arXiv]

P Sundaresan, R Antonova, J Bohg. DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation and Rendering of Deformable Objects. IEEE International Conference on Intelligent Robots and Systems (IROS), 2022.

J Yang, J Zhang, C Settle, A Rai, R Antonova, J Bohg. Learning Periodic Tasks from Human Demonstrations. IEEE International Conference on Robotics and Automation (ICRA), 2022.

R. Antonova *, P. Shi *, H. Yin, Z. Weng, D. Kragic. Dynamic Environments with Deformable Objects. Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2021.

R. Antonova, F. Ramos, R. Possas, D. Fox. BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym, arXiv:2107.04527, 2021. This open source framework was presented as part of the full-day tutorial on End-to-end GPU-accelerated Learning and Control for Robotics with Isaac Gym at RSS, 2021. [Video]

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. Presented at the Conference on Mathematical Theory of Deep Neural Networks (DeepMath), 2020.

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), PMLR 100: 456-465, 2019

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. Selected for oral presentation (7.5% acceptance rate).

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 . 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 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. [workshop website]

R. Antonova. Multi-task Value of Information Planning for Sequential Multi-task Bandits. Technical Report. Robotics Institute, Carnegie Mellon University, 2016. [related WiML poster]

Blue Sky Ideas

R. Antonova, A. Handa. Robots Teaching Humans: A New Communication Paradigm via Reverse Teleoperation. International Conference on Autonomous Agents and Multiagent Systems (AAAMAS), 2022.

R. Antonova, J. Bohg. Learning to be Multimodal : Co-evolving Sensory Modalities and Sensor Properties. Conference on Robot Learning, PMLR 164:1782-1788, 2022.

Conference and Workshop Organization

Part of the IEEE Robotics and Automation (RAS) Technical Committee on Robot Learning (junior co-chair).

Part of the organizing committee for Learning for Dynamics and Control (L4DC) 2022 conference.

Program Committee Chair for RSS Pioneers 2021.

One of core organizers for the Physical Reasoning and Inductive Biases for the Real World NeurIPS 2021 workshop.

Co-organizer of Representing and Manipulating Deformable Objects ICRA 2021 workshop.

Co-organizer of a full-day tutorial End-to-end GPU-accelerated Learning and Control for Robotics with Isaac Gym at the Robotics: Science and Systems Conference, 2021.

Past Master's Thesis Supervision

Variational Swarm Reinforcement Learning for Predictive Models of Deformable Objects. Simon Reisch. Karlsruhe Institute of Technology Master's Thesis, September 2023

Reinforcement Learning for Dexterity Transfer Between Manipulators. Carlo Rapisarda. KTH Master's Thesis, June 2019

Converting Deterministic Simulators to Realistic Stochastic Models via Data Alignment. Martin Hwasser. KTH Master's Thesis, June 2019

Our Research in the News