Episode 121 – Adaptable robots for the home

Claire chatted to Lerrel Pinto from New York University about using machine learning to train robots to adapt to new environments.

Lerrel Pinto is an Assistant Professor of Computer Science at New York University (NYU). His research is aimed at getting robots to generalize and adapt in the messy world we live in. His lab focuses broadly on robot learning and decision making, with an emphasis on large-scale learning (both data and models); representation learning for sensory data; developing algorithms to model actions and behaviour; reinforcement learning for adapting to new scenarios; and building open-source, affordable robots.

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Tagged as: Artificial intelligence, Computer vision, Human robot interaction, Domestic, Home, Generalisation.