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IRIM Seminar Series Session 2 | Sep. 30, 2020
All Seminar Sessions Occur @ 12:15 - 1:15 (EST)

"Star Wars: The Rise of Robots and Intelligent Machines”

Abstract: A long time ago, in a galaxy far far away, a space opera movie captured the imaginations of roboticists, researchers, and writers from around the world.  Over the last 43 years, Star Wars has had an immense impact on our collective perception of robotics.  It has introduced some of the most beloved droids as well as one of the most feared cyborgs in science fiction.  In this panel, we will discuss how the Star Wars movies have influenced the design of robots and intelligent machines, including prosthetics, cybernetics, and artificial intelligence.  We will show examples of how George Lucas portrayed good and evil in different types of technology and how he depicted human-robot teaming.  These illustrations have driven how we design and interact with technology to this day.  Whether you love or love-to-hate the movies, these are the droids discussions that you are looking for!

Panelists: Matthew Gombolay - GT IC, Ellen Mazumdar - GT ME , Lisa Yaszek - GT Media & Communications, Aaron Young - GT ME

Access the Debate Here:

Save the Date: Industry & Student Virtual Mixer

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Speaker 1 | Probabilistic Reasoning on Lie Groups with Application to Nonparametric Object and Parts Modeling
David Hayden  | Computer Science & Artificial Intelligence Laboratory,  Massachusetts Institute of Technology

Abstract: Articulated motion analysis often utilizes strong prior knowledge such as a known or trained parts model for humans. Yet, the world contains a variety of articulating objects--mammals, insects, mechanized structures--where the number and configuration of parts for a particular object is unknown in advance. Here, we relax such strong assumptions via an unsupervised, Bayesian nonparametric parts model that infers an unknown number of parts with motions coupled by a body dynamic and parameterized by SE(D), the Lie group of rigid transformations. We derive an inference procedure that utilizes short observation sequences (image, depth, point cloud or mesh) of an object in motion without need for markers or learned body models. Novel and efficient Gibbs decompositions for inference over distributions on SE(D) demonstrate robust part decompositions of moving objects under both 3D and 2D observation models. The inferred representation permits new analysis, such as object segmentation by relative part motion, and transfers to new observations of the same object type. Although this talk focuses on SO(D) and SE(D), we introduce probabilistic reasoning over general matrix Lie groups.

Speaker 2 | Human Pose Estimation in Bed
Henry M. Clever | Healthcare Robotics Lab, Georgia Institute of Technology

Abstract: People spend a substantial part of their lives at rest in bed. 3D human pose and shape estimation for this activity would be beneficial to numerous applications, including remote patient care, bed sore management, and assistive robotics. However, this is a challenging perception problem due to a variety of factors, including bedding covering the body, nearby medical equipment, and the unavailability of well-labeled perceptual data. To overcome these challenges, we use a pressure sensing array on the bed to sense the body in a manner that is insensitive to bedding, and physics simulations to automatically generate synthetic perceptual data at scale with labels. We also develop novel deep learning models, including a model that infers body shape and pose from a real pressure image when trained exclusively on synthetic data. Going forward, we propose new investigations into the use of a depth sensing camera above the bed to complement the pressure sensing array, and the use of our estimation methods for assistive robotics application.

Access the Session Here:  tinyurl.com/F20robograds