Title: Control for Embodied Learning
Speaker: Todd Murphey, Northwestern University
Abstract: Embodied learning systems rely on motion synthesis to enable efficient and flexible learning during continuous online deployment. Motion motivated by learning needs can be found throughout natural systems, yet there is surprisingly little known about synthesizing motion to support learning for robotic systems. Moreover, robotic systems will need to collect data autonomously for learning, for instance when isolated for long period of time or when encountering novel environmental features. Learning goals create a distinct set of control-oriented challenges, including how to choose measures as objectives, synthesize real-time control based on these objectives, impose physics-oriented constraints on learning, and produce analyses that certify performance and safety with limited knowledge. This talk will discuss learning tasks that robots encounter, abstractions that enable regulating information content of observations, and recent progress on algorithms for generating action plans that facilitate learning.
Zoom: https://northwestern.zoom.us/j/96919208870
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Ted Shaeffer
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