If you’ve ever watched a large flock of birds on the wing, moving across the sky like a cloud with various shapes and directional changes appearing from seeming chaos, or the maneuvers of an ant colony forming bridges and rafts to escape floods, you’ve been observing what scientists call self-organization. What may not be as obvious is that self-organization occurs throughout the natural world, including bacterial colonies, protein complexes, and hybrid materials. Understanding and predicting self-organization, especially in systems that are out of equilibrium, like living things, is an enduring goal of statistical physics.

This goal is the motivation behind a recently introduced principle of physics called rattling, which posits that systems with sufficiently “messy” dynamics organize into what researchers refer to as low rattling states. Although the principle has proved accurate for systems of robot swarms, it has been too vague to be more broadly tested, and it has been unclear exactly why it works and to what other systems it should apply.

Dana Randall, a professor in the School of Computer Science, and Jacob Calvert, a postdoctoral fellow at the Institute for Data Engineering and Science, have formulated a theory of rattling that answers these fundamental questions. Their paper, “A Local-Global Principle for Nonequilibrium Steady States,” published last week in Proceedings of the National Academy of Sciences, characterizes how rattling is related to the amount of time that a system spends in a state. Their theory further identifies the classes of systems for which rattling explains self-organization.

When we first heard about rattling from physicists, it was very hard to believe it could be true. Our work grew out of a desire to understand it ourselves. We found that the idea at its core is surprisingly simple and holds even more broadly than the physicists guessed.

Dana Randall  Professor, School of Computer Science & Adjunct Professor, School of Mathematics 
Georgia Institute of Technology 

Beyond its basic scientific importance, the work can be put to immediate use to analyze models of phenomena across scientific domains. Additionally, experimentalists seeking organization within a nonequilibrium system may be able to induce low rattling states to achieve their desired goal. The duo thinks the work will be valuable in designing microparticles, robotic swarms, and new materials. It may also provide new ways to analyze and predict collective behaviors in biological systems at the micro and nanoscale.

The preceding material is based on work supported by the Army Research Office under award ARO MURI Award W911NF-19-1-0233 and by the National Science Foundation under grant CCF-2106687. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsoring agencies.

Jacob Calvert and Dana Randall. A local-global principle for nonequilibrium steady states. Proceedings of the National Academy of Sciences, 121(42):e2411731121, 2024.

The Institute for Robotics and Intelligent Machines (IRIM) launched a new initiatives program, starting with several winning proposals, with corresponding initiative leads that will broaden the scope of IRIM’s research beyond its traditional core strengths. A major goal is to stimulate collaboration across areas not typically considered as technical robotics, such as policy, education, and the humanities, as well as open new inter-university and inter-agency collaboration routes. In addition to guiding their specific initiatives, these leads will serve as an informal internal advisory body for IRIM. Initiative leads will be announced annually, with existing initiative leaders considered for renewal based on their progress in achieving community building and research goals. We hope that initiative leads will act as the “faculty face” of IRIM and communicate IRIM’s vision and activities to audiences both within and outside of Georgia Tech.

Meet 2024 IRIM Initiative Leads

Stephen Balakirsky; Regents' Researcher, Georgia Tech Research Institute & Panagiotis Tsiotras; David & Andrew Lewis Endowed Chair, Daniel Guggenheim School of Aerospace Engineering | Proximity Operations for Autonomous Servicing

Why It Matters: Proximity operations in space refer to the intricate and precise maneuvers and activities that spacecraft or satellites perform when they are in close proximity to each other, such as docking, rendezvous, or station-keeping. These operations are essential for a variety of space missions, including crewed spaceflights, satellite servicing, space exploration, and maintaining satellite constellations. While this is a very broad field, this initiative will concentrate on robotic servicing and associated challenges. In this context, robotic servicing is composed of proximity operations that are used for servicing and repairing satellites in space. In robotic servicing, robotic arms and tools perform maintenance tasks such as refueling, replacing components, or providing operation enhancements to extend a satellite's operational life or increase a satellite’s capabilities.

Our Approach: By forming an initiative in this important area, IRIM will open opportunities within the rapidly evolving space community. This will allow us to create proposals for organizations ranging from NASA and the Defense Advanced Research Projects Agency to the U.S. Air Force and U.S. Space Force. This will also position us to become national leaders in this area. While several universities have a robust robotics program and quite a few have a strong space engineering program, there are only a handful of academic units with the breadth of expertise to tackle this problem. Also, even fewer universities have the benefit of an experienced applied research partner, such as the Georgia Tech Research Institute (GTRI), to undertake large-scale demonstrations. Georgia Tech, having world-renowned programs in aerospace engineering and robotics, is uniquely positioned to be a leader in this field. In addition, creating a workshop in proximity operations for autonomous servicing will allow the GTRI and Georgia Tech space robotics communities to come together and better understand strengths and opportunities for improvement in our abilities.

Matthew Gombolay; Assistant Professor, Interactive Computing | Human-Robot Society in 2125: IRIM Leading the Way

Why It Matters: The coming robot “apocalypse” and foundation models captured the zeitgeist in 2023 with “ChatGPT” becoming a topic at the dinner table and the probability occurrence of various scenarios of AI driventechnological doom being a hotly debated topic on social media. Futuristic visions of ubiquitous embodied Artificial Intelligence (AI) and robotics have become tangible. The proliferation and effectiveness of first-person view drones in the Russo-Ukrainian War, autonomous taxi services along with their failures, and inexpensive robots (e.g., Tesla’s Optimus and Unitree’s G1) have made it seem like children alive today may have robots embedded in their everyday lives. Yet, there is a lack of trust in the public leadership bringing us into this future to ensure that robots are developed and deployed with beneficence.

Our Approach: This proposal seeks to assemble a team of bright, savvy operators across academia, government, media, nonprofits, industry, and community stakeholders to develop a roadmap for how we can be the most trusted voice to guide the public in the next 100 years of innovation in robotics here at the IRIM. We propose to carry out specific activities that include conducting the activities necessary to develop a roadmap about Robots in 2125: Altruistic and Integrated Human-Robot Society. We also aim to build partnerships to promulgate these outcomes across Georgia Tech’s campus and internationally.

Gregory Sawicki; Joseph Anderer Faculty Fellow, School of Mechanical Engineering & Aaron Young; Associate Professor, Mechanical Engineering | Wearable Robotic Augmentation for Human Resilience 

Why It Matters: The field of robotics continues to evolve beyond rigid, precision-controlled machines for amplifying production on manufacturing assembly lines toward soft, wearable systems that can mediate the interface between human users and their natural and built environments. Recent advances in materials science have made it possible to construct flexible garments with embedded sensors and actuators (e.g., exosuits). In parallel, computers continue to get smaller and more powerful, and state-of-the art machine learning algorithms can extract useful information from more extensive volumes of input data in real time. Now is the time to embed lean, powerful, sensorimotor elements alongside high-speed and efficient data processing systems in a continuous wearable device.

Our Approach: The mission of the Wearable Robotic Augmentation for Human Resilience (WeRoAHR) initiative is to merge modern advances in sensing, actuation, and computing technology to imagine and create adaptive, wearable augmentation technology that can improve human resilience and longevity across the physiological spectrum — from behavioral to cellular scales. The near-term effort (~2-3 years) will draw on Georgia Tech’s existing ecosystem of basic scientists and engineers to develop WeRoAHR systems that will focus on key targets of opportunity to increase human resilience (e.g., improved balance, dexterity, and stamina). These initial efforts will establish seeds for growth intended to help launch larger-scale, center-level efforts (>5 years).

Panagiotis Tsiotras; David & Andrew Lewis Endowed Chair, Daniel Guggenheim School of Aerospace Engineering & Sam Coogan; Demetrius T. Paris Junior Professor, School of Electrical and Computer Engineering | Initiative on Reliable, Safe, and Secure Autonomous Robotics 

Why It Matters: The design and operation of reliable systems is primarily an integration issue that involves not only each component (software, hardware) being safe and reliable but also the whole system being reliable (including the human operator). The necessity for reliable autonomous systems (including AI agents) is more pronounced for “safety-critical” applications, where the result of a wrong decision can be catastrophic. This is quite a different landscape from many other autonomous decision systems (e.g., recommender systems) where a wrong or imprecise decision is inconsequential.

Our Approach: This new initiative will investigate the development of protocols, techniques, methodologies, theories, and practices for designing, building, and operating safe and reliable AI and autonomous engineering systems and contribute toward promoting a culture of safety and accountability grounded in rigorous objective metrics and methodologies for AI/autonomous and intelligent machines designers and operators, to allow the widespread adoption of such systems in safety-critical areas with confidence. The proposed new initiative aims to establish Tech as the leader in the design of autonomous, reliable engineering robotic systems and investigate the opportunity for a federally funded or industry-funded research center (National Science Foundation (NSF) Science and Technology Centers/Engineering Research Centers) in this area.

Colin Usher; Robotics Systems and Technology Branch Head, GTRI | Opportunities for Agricultural Robotics and New Collaborations

Why It Matters: The concepts for how robotics might be incorporated more broadly in agriculture vary widely, ranging from large-scale systems to teams of small systems operating in farms, enabling new possibilities. In addition, there are several application areas in agriculture, ranging from planting, weeding, crop scouting, and general growing through harvesting. Georgia Tech is not a land-grant university, making our ability to capture some of the opportunities in agricultural research more challenging. By partnering with a land-grant university such as the University of Georgia (UGA), we can leverage this relationship to go after these opportunities that, historically, were not available.

Our Approach: We plan to build collaborations first by leveraging relationships we have already formed within GTRI, Georgia Tech, and UGA. We will achieve this through a significant level of networking, supported by workshops and/or seminars with which to recruit faculty and form a roadmap for research within the respective universities. Our goal is to identify and pursue multiple opportunities for robotics-related research in both row-crop and animal-based agriculture. We believe that we have a strong opportunity, starting with formalizing a program with the partners we have worked with before, with the potential to improve and grow the research area by incorporating new faculty and staff with a unified vision of ubiquitous robotics systems in agriculture. We plan to achieve this through scheduled visits with interested faculty, attendance at relevant conferences, and ultimately hosting a workshop to formalize and define a research roadmap.

Ye Zhao; Assistant Professor, School of Mechanical Engineering | Safe, Scalable, and Sustainable Human-Robot Teaming: Interaction, Synergy, and Augmentation

Why It Matters: Collaborative robots in unstructured environments such as construction and warehouse sites show great promise in working with humans on repetitive and dangerous tasks to improve efficiency and productivity. However, preprogrammed and nonflexible interaction behaviors of existing robots lower the naturalness and flexibility of the collaboration process. Therefore, it is crucial to improve physical interaction behaviors of the collaborative human-robot teaming.

Our Approach: This proposal will advance the understanding of the bi-directional influence and interaction of human-robot teaming for complex physical activities in dynamic environments by developing new methods to predict worker intention via multi-modal wearable sensing, reasoning about complex human-robot-workspace interaction, and adaptively planning the robot’s motion considering both human teaming dynamics and physiological and cognitive states. More importantly, our team plans to prioritize efforts to (i) broaden the scope of IRIM’s autonomy research by incorporating psychology, cognitive, and manufacturing research not typically considered as technical robotics research areas; (ii) initiate new IRIM education, training, and outreach programs through collaboration with team members from various Georgia Tech educational and outreach programs (including Engaging New Generations at Georgia Tech through Engineering and Science; Vertically Integrated Projects; and Center for Education Integrating Science, Mathematics, and Computing) as well as the Atlanta University Center Consortium (the world’s largest consortium of African American private institutions of higher education) which comprises Clark Atlanta University, Morehouse College, and Spelman College; and (iii) aim for large governmental grants such as Department of Defense Multidisciplinary University Research Initiative, NSF Research Trainee program, and NSF Future of Work programs.

-Christa M. Ernst

Hepatic, or liver, disease affects more than 100 million people in the U.S. About 4.5 million adults (1.8%) have been diagnosed with liver disease, but it is estimated that between 80 and 100 million adults in the U.S. have undiagnosed fatty liver disease in varying stages. Over time, undiagnosed and untreated hepatic diseases can lead to cirrhosis, a severe scarring of the liver that cannot be reversed. 

Most hepatic diseases are chronic conditions that will be present over the life of the patient, but early detection improves overall health and the ability to manage specific conditions over time. Additionally, assessing patients over time allows for effective treatments to be adjusted as necessary. The standard protocol for diagnosis, as well as follow-up tissue assessment, is a biopsy after the return of an abnormal blood test, but biopsies are time-consuming and pose risks for the patient. Several non-invasive imaging techniques have been developed to assess the stiffness of liver tissue, an indication of scarring, including magnetic resonance elastography (MRE).

MRE combines elements of ultrasound and MRI imaging to create a visual map showing gradients of stiffness throughout the liver and is increasingly used to diagnose hepatic issues. MRE exams, however, can fail for many reasons, including patient motion, patient physiology, imaging issues, and mechanical issues such as improper wave generation or propagation in the liver. Determining the success of MRE exams depends on visual inspection of technologists and radiologists. With increasing work demands and workforce shortages, providing an accurate, automated way to classify image quality will create a streamlined approach and reduce the need for repeat scans. 

Professor Jun Ueda in the George W. Woodruff School of Mechanical Engineering and robotics Ph.D. student Heriberto Nieves, working with a team from the Icahn School of Medicine at Mount Sinai, have successfully applied deep learning techniques for accurate, automated quality control image assessment. The research, “Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results,” was published in the Journal of Magnetic Resonance Imaging.

Using five deep learning training models, an accuracy of 92% was achieved by the best-performing ensemble on retrospective MRE images of patients with varied liver stiffnesses. The team also achieved a return of the analyzed data within seconds. The rapidity of image quality return allows the technician to focus on adjusting hardware or patient orientation for re-scan in a single session, rather than requiring patients to return for costly and timely re-scans due to low-quality initial images.

This new research is a step toward streamlining the review pipeline for MRE using deep learning techniques, which have remained unexplored compared to other medical imaging modalities.  The research also provides a helpful baseline for future avenues of inquiry, such as assessing the health of the spleen or kidneys. It may also be applied to automation for image quality control for monitoring non-hepatic conditions, such as breast cancer or muscular dystrophy, in which tissue stiffness is an indicator of initial health and disease progression. Ueda, Nieves, and their team hope to test these models on Siemens Healthineers magnetic resonance scanners within the next year.

Publication
Nieves-Vazquez, H.A., Ozkaya, E., Meinhold, W., Geahchan, A., Bane, O., Ueda, J. and Taouli, B. (2024), Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results. J Magn Reson Imaging. https://doi.org/10.1002/jmri.29490

Prior Work 
Robotically Precise Diagnostics and Therapeutics for Degenerative Disc Disorder

Related Material
Editorial for “Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results”