Research
I work on unsupervised learning for robotics.
I am fascinated by the following question: how can robots autonomously learn from unlabeled experience in unstructured environments, with no external supervision at all?
More specifically, I study what objectives intelligent agents should optimize to learn from the widest range of experience, and how those objectives should be optimized such that neural scaling laws can emerge, meaning that the model performance automatically improves given more (unlabeled) data and compute.
In the context of robotics, I think a lot about unsupervised perception, control, and planning.
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