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Episode 121: Adaptable robots for the home - Lerrel Pinto
Manage episode 483119269 series 3562260
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.
Join the Robot Talk community on Patreon: https://www.patreon.com/ClaireAsher
123 episodes
Manage episode 483119269 series 3562260
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.
Join the Robot Talk community on Patreon: https://www.patreon.com/ClaireAsher
123 episodes
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