Rika Antonova

(University of Cambridge)

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When? Wednesday 27th November, 2pm - 3pm

 

Where? Huxley 145 (Directions: click here)

 

Talk Title: The Ingredients for Efficient Robot Learning and Exploration

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Abstract: In this talk, I will outline ingredients for enabling efficient robot learning. First, I will demonstrate how large vision-language models can enhance scene understanding and generalization, allowing robots to learn general rules from specific examples for handling everyday objects. Then, I will describe a policy learning method that leverages equivariance to significantly reduce the amount of training data needed for learning from human demonstrations. Moving beyond learning from demonstrations, we will explore how simulation can enable robots to learn autonomously. I will describe the challenges and opportunities of bringing differentiable simulators closer to reality, and contrast direct controller optimization in such adaptive simulators with reinforcement learning in 'black-box' simulators. To further expand robot capabilities, we will consider adapting hardware. In particular, I will demonstrate how differentiable simulation can be used for learning tool morphology to automatically adapt tools for robots. Finally, I will outline a vision of how new affordable and robust sensors can aid in learning and control, how rapid prototyping can enable effective design iterations, and how scaling up exploration would let us tackle the vast design space of optimizing sensing, morphology, actuation, and policy learning jointly. I will conclude with examples of interdisciplinary collaborations where hardware, control, learning, and vision researchers jointly build solutions greater than the sum of their parts.

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Bio: Rika Antonova is an Associate Professor at the University of Cambridge. Her research interests include data-efficient reinforcement learning algorithms, active learning & exploration​, ​and ​robotics. Earlier, Rika was a postdoctoral scholar at Stanford University upon receiving the NSF/CRA Computing Innovation Fellowship from the US National Science Foundation. Rika completed her PhD at KTH, Stockholm in the division of "Robotics, Perception, and Learning". Earlier, she obtained a research Master's degree from the Robotics Institute at Carnegie Mellon University. Before that, Rika was a senior software engineer at Google in the Search Personalization team and then in the Character Recognition team (developing open-source OCR engine Tesseract). 

Webpage: https://contactrika.github.io