A key, unsolved challenge for learning with real robotic systems is the ability to acquire vision-based behaviors from raw RGB images that can generalize to new objects and new goals. We present two approaches to this goal that we plan to demonstrate: first, learning task-agnostic visual models for planning, which can generalize to new objects and goals, and second, learning to quickly adapt to new objects and environments using meta-imitation learning. In essence, these two approaches seek to generalize and dynamically adapt to new settings, respectively, as we discuss next.
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Tue Dec 05 07:00 PM -- 10:30 PM (PST) @ Pacific Ballroom Concourse #D6
Deep Robotic Learning using Visual Imagination and Meta-Learning
In Demos Tues