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DTSTART;TZID=America/New_York:20220126T150000
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SUMMARY:Spring 2022 GRASP SFI: Paloma Sodhi\, Carnegie Mellon University\, "Learning in factor graphs for tactile perception"
DESCRIPTION:*This will be a HYBRID Event with in-person attendance in Levine 307 and Virtual attendance via Zoom \nFactor graphs offer a flexible and powerful framework for solving large-scale\, nonlinear inference problems as encountered in robot perception. Typically these methods rely on handcrafted models that are efficient to optimize. However\, robots often perceive the world through complex\, high-dimensional sensor observations. For instance\, consider a robot manipulating an object in-hand and receiving high-dimensional tactile observations from which it must infer latent object poses. Can we learn models for such observations directly from sensor data? \nIn this talk\, I will discuss algorithms and representations for learning observation models end-to-end with optimizers in the loop. I will present a novel approach\, LEO\, that casts the problem of learning observation models as cost function learning that makes no assumptions on the differentiability of the underlying optimizer. I will also discuss different feature representations for extracting salient information from tactile image observations. We will evaluate these approaches on a real-world application of tactile perception for robot manipulation where we demonstrate reliable object tracking in hundreds of trials across planar pushing and in-hand manipulation tasks.
URL:https://seasevents.nmsdev7.com/event/spring-2022-grasp-sfi-paloma-sodhi-learning-in-factor-graphs-for-tactile-perception/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
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