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Spring 2023 GRASP SFI: Michael Chang, University of California, Berkeley, “Neural Software Abstractions: Learning Abstractions for Automatically Modeling and Manipulating Systems”
March 22 at 3:00 PM - 4:00 PM
This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom. This week’s presenter will be in-person as well.
While it is tempting to view robotics as a nail that can be solved with the deep learning hammer, we have seen that deep-learning based perception and action pipelines for robots are notoriously brittle and data hungry. In this talk, I advocate for a more measured approach for designing data-driven controllers by focusing learning on task-relevant portions of the MDP. Through this philosophy, I show that we can acquire capable learning systems that can transfer between morphologically distinct robots, intelligently probe the environment for imperceptible reward signals, and perform deep exploration with no priors.
University of California, Berkeley
Michael is a graduating PhD student at UC Berkeley advised by Sergey Levine and Tom Griffiths. Before his PhD he was an undergraduate researcher in Josh Tenenbaum’s group at MIT. He has also interned at DeepMind, Meta, Jürgen Schmidhuber’s group, and Honglak Lee’s group. His research goals are twofold: (1) to enable machines to construct their own abstractions for automatically modeling and manipulating systems and (2) to develop adaptive human-computer interfaces that bridge the gap between modalities that humans understand (e.g. language, diagrams) and modalities that computers understand (i.e. code).