Rika Antonova

Dr Rika Antonova is an Associate Professor at the University of Cambridge, Department of Computer Science and Technology. She leads the Cambridge Resilient Autonomous Learning (CamRAL) Lab. Her research interests include Machine Learning and Artificial Intelligence, Mobile Systems, Robotics and Automation. Dr Antonova has worked at Stanford University, KTH (Sweden), NVIDIA Robotics, Microsoft Research, and Google. She has also been affiliated with the Robotics Institute at Carnegie Mellon University. Her current work focuses on robotics and reinforcement learning algorithms, with an emphasis on data-efficient methods and decision-making for scientific and environmental domains.

Rika Antonova is an Associate Professor at the University of Cambridge, specializing in Transfer Learning, Reinforcement Learning, and Robotics. Her research focuses on developing efficient and generalizable learning methods for robotic systems, particularly through the integration of simulation and real-world data. She explores co-design approaches that enhance the adaptability of robots in dynamic environments. Her work includes benchmarking robotic manipulation tasks, improving sample efficiency in reinforcement learning, and leveraging Bayesian optimization for complex robotic control. Antonova's research also addresses challenges in active learning and equivariant policies for visuomotor control, with applications in deformable object manipulation and human-robot interaction.