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Broadly, my interests lie in the intersection of robotics and machine learning. In my PhD I focus on improving the sample efficiency and robustness of learnt models through utilising inductive biases in the context of robotics applications. In the past, I have studied ways of building such biases through learning modular and re-usable across tasks world models [1, 2]; demonstrations [3,4]; transfer learning [5]; and self-supervised reinforcement learning [6]. I have particular interest in fast contact-rich skill acquisitions, such as peg, gear and plug insertions. In other work I have also studied ways to improve 2D trajectory generation in crowded and collaborative settings as well as extracting representations that are task specific and robust to perturbations. With this, I am generally excited about projects combining ML/RL/DL and robotics, including working on algorithms, simulations, actual robot experimentation, and the relevant software/hardware environments.
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IEEE Robotics and Automation Lettersno. 2 (2020): 2872-2872
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