View this email in your browser
Visit us on the web

News from IRIM | September 23, 2020 Edition
Robotics Events @ GT

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:
Robograds Virtual Seminar Logo
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:



IRIM Research Highlight | Stretchable Nanocomposite Sensors, Nanomembrane Interconnectors, and Wireless Electronics toward Feedback–Loop Control of a Soft Earthworm Robot

In new collaborative research work from Frank Hammond (GT-ME), Dan Goldman (GT-Physics) and W.H. Yeo (GT-ME), this earthworm robot squishes it's way through an obstacle course. The team, working with  a soft, stretchable nanocomposite system with built-in wireless electronics aimed to achieve feedback–loop motion control of a robotic earthworm. The bot's nanostructured strain sensor, based on a carbon nanomaterial and a low-modulus silicone elastomer, allows for seamless integration with the body of the soft robot that can accommodate large strains caused by bending, stretching, and physical interactions with obstacle.

The miniaturized wireless circuit, embedded in the robot joint, offers real-time monitoring of strain changes during the motions of a robotic segment. Collectively, the soft sensor system presented in this work shows great potential to be integrated with other flexible, stretchable electronics for applications in soft robotics, wearable devices, and human-machine interfaces.

See the Research Paper Here

Introducing IRIM's Newest Faculty Member:

Glenn Lightsey

Dr. Glenn Lightsey is the director of the Space Systems Design Lab and director of the Center for Space Technology And Research at Georgia Tech. His research program focuses on the technology of small satellites, including: guidance, navigation, and control systems; attitude determination and control; formation flying, satellite swarms, and satellite networks; cooperative control; proximity operations and unmanned spacecraft rendezvous; space based Global Positioning System receivers; radionavigation; visual navigation; propulsion; satellite operations; and space systems engineering. Dr. Lightsey has authored and co-authored more than 140 technical articles and publications, including four book chapters. He is an AIAA Fellow and a Founding Member of the AIAA Small Satellite Technical Committee. He is Associate Editor-in-Chief of the Journal of Small Satellites. Dr. Lightsey was previously employed at the University of Texas at Austin and NASA’s Goddard Space Flight Center.

Prior to joining the faculty at the Daniel Guggenheim School of Aerospace Engineering, Dr. Lightsey held the position of Fellow of the W. R. Woolrich Professor in Engineering in the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin. He also held the title of University Distinguished Teaching Professor, a position designated to less than 5% of the tenured faculty at The University of Texas. In 2011, Dr. Lightsey received the American Society for Engineering Education's John Leland Atwood Award for outstanding aerospace engineering education, and the William David Blunk Memorial Professorship for outstanding undergraduate teaching at the University of Texas at Austin

Representative Selection of Work:
  1. Stevenson, T.H.; and Lightsey, E.G.; “Design and Optimization of a Multifunctional 3D-Printed Structure for an Inspector Cubesat,” Acta Astronautica, Vol. 170, pp. 331-341, May, 2020.
  2. Lightsey, E.G.; Stevenson, T.; and Sorgenfrei, M.; "Development and Testing of a 3-D-Printed Cold Gas Thruster for an Interplanetary CubeSat," Proceedings of the IEEE, Vol. 106, No. 3, pp. 379-390, March, 2018.
  3. Eldad, O.; Lightsey, E.G.; and Claudel, C.; "Minimum-Time Attitude Control of Deformable Solar Sails with Model Uncertainty," Journal of Spacecraft and Rockets, Vol. 54, No. 4, pp. 863-870, July, 2017.


Visit Glenn Lightsey's Website Here

Calling all IROS 2020 Participants

Are you presenting at IROS 2020? IRIM wants to know! Drop us a line at with your accepted paper details, presentation time and link, or other pertinent information and we will publicize on our social and news channels running up to the conference.
IRIM Faculty Lab Highlight

The Cognitive Engineering Center (CEC)

PI | Karen Feigh , Ph.D.
Associate Professor | Daniel Guggenheim School of Aerospace Engineering

EPIC Lab Team


In our complex world of humans and machines, CEC researchers are building the foundations, training, and technologies for safe and effective work.

Our research is built around a simple question: What if we designed training and technology based on how people actually do their work? In answering this question, we have contributed to procedures and displays for air and space operations, to basic research for military command and control decision making, and adaptive intelligence for industrial robots and autonomous vehicles. We examine human-machine interaction using any tool necessary (field work or human-subjects studies; and interviews, software, or mathematics), finding the appropriate solution for the requirements.

Founded in 2005 by Dr. Amy Pritchett in the Georgia Tech School of Aerospace Engineering, and now, in our second decade, the CEC continues to ignore the boundaries of traditional disciplines in our search for meaningful, implimentable solutions. Aerospace engineers, computer scientists, roboticists, industrial engineers, and education researchers work together to build a safer and more effective human-machine world.

Access the CEC Here

Institute for Robotics and Intelligent Machines (IRIM)
Klaus Advanced Computing Building
266 Ferst Drive
Atlanta, GA 30332-3000

Want to change how you receive these emails?
You can update your preferences or unsubscribe from this list.

Copyright © 2020 Institute for Robotics and Intelligent Machines (IRIM) at Georgia Tech
All rights reserved.