Smith International Professorship in Mechanical Engineering and Professor of Aerospace and Mechanical Engineering and Computer Science
- 1994, Doctoral Degree, Mechanical Engineering, University of Maryland College Park
- 1989, Master's Degree, Production Engineering, Indian Institute of Technology-Delhi
- 1988, Bachelor's Degree, Mechanical Engineering, Indian Institute of Technology-Roorkee
Dr. Satyandra K. Gupta holds Smith International Professorship in the Aerospace and Mechanical Engineering Department and the Department of Computer Science in the Viterbi School of Engineering at the University of Southern California. He is also the founding director of the Center for Advanced Manufacturing at the University of Southern California. Prior to joining the University of Southern California, he was a Professor in the Department of Mechanical Engineering and the Institute for Systems Research at the University of Maryland, College Park. He was also the founding director of the Maryland Robotics Center and the Advanced Manufacturing Laboratory at the University of Maryland. In addition, he served as a Program Director for the National Robotics Initiative at the National Science Foundation from September 2012 to September 2014. Before joining the University of Maryland, he was a Research Scientist in the Robotics Institute at Carnegie Mellon University.
Dr. Gupta received a Bachelor of Engineering (B.E.) degree in Mechanical Engineering from the University of Roorkee (currently known as the Indian Institute of Technology, Roorkee) in 1988. He received a Gold Medal for securing the first rank in his B.E. class (1988) and a Gold Medal for the best Engineering Design Project (1988). He received a Master of Technology (M. Tech.) in Production Engineering from the Indian Institute of Technology, Delhi, in 1989. He received a Ph.D. in Mechanical Engineering from the University of Maryland, College Park, in 1994. During his Ph.D. study, he was awarded a Graduate School Fellowship.
Dr. Gupta's research is focused on developing human-centered automation to increase human productivity, reduce health risks for humans, and enable innovation. He is developing the next generation of robotic solutions that can serve as smart assistants for humans and creating decision support systems that empower humans to make more informed decisions in a timely manner. He is making advances in physics-informed artificial intelligence and machine learning to realize smart robotic assistants and decision support systems that operate in domains characterized by model uncertainty, complex physics, and fast decision-making speeds. His team's primary research applications include multi-agent environment monitoring and information gathering, computer-aided design, and smart manufacturing. He is currently developing smart robotic assistants for a wide variety of manufacturing operations such as assembly, composite prepreg layup, kitting, finishing, inspection, and machine tending. He is also designing robotic cells for multiscale additive manufacturing.
He is best known for the following five notable contributions. First, he holds a U.S. Patent titled Apparatus and Method for Multi-Purpose Setup Planning for Sheet Metal Bending Operations. Japanese machine tool manufacturer Amada used this patent to develop automated process planning software for robotic pressbrakes, widely used in the sheet metal industry. Second, he led the development of an in-mold assembly process to realize geometrically complex heterogeneous structures cost-effectively. These advances have enabled the use of novel, bio-inspired design concepts in applications such as robotics, bio-medical devices, thermal management systems, and aerospace structures. Third, by integrating perception, planning, and control, his group has turned optical tweezers into robots to precisely manipulate microscale objects. This micromanipulation capability is useful for safely manipulating biological cells and significantly reduces the human effort needed to conduct biological experiments. Fourth, his group invented Robo Raven, the first robotic bird capable of flying outdoor using independent wing control and performing aerobatic maneuvers. Finally, his group developed smart robotic assistants for increasing human productivity and reducing human health risks for high-mix manufacturing applications. GrayMatter Robotics, a company Dr. Gupta co-founded with his Ph.D. students, has commercialized robotic sanding and polishing solutions. He has authored more than four hundred articles in journals, conference proceedings, and book chapters. In addition, he has delivered more than one hundred and sixty invited seminars and lectures.
Dr. Gupta is a fellow of the American Society of Mechanical Engineers (ASME), Institute of Electrical and Electronics Engineers (IEEE), Society of Manufacturing Engineers (SME), and Solid Modeling Association (SMA). He serves as the Editor-in-Chief of the ASME Journal of Computing and Information Science in Engineering and as the Editor-in-Chief of the Advanced Manufacturing Book Series by World Scientific Publishing Company. He also serves as a member of the Technical Advisory Committee for Advanced Robotics for Manufacturing (ARM) Institute and a member of the National Materials and Manufacturing Board. He has served as a member of the Autonomy Summer Study Task Force for the Defense Science Board.
Dr. Gupta has received numerous honors and awards for his contributions to the scientific community. Representative examples include: the Young Investigator Award from the Office of Naval Research in 2000, Robert W. Galvin Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers in 2001, CAREER Award from the National Science Foundation in 2001, Presidential Early Career Award for Scientists and Engineers (PECASE) in 2001, Invention of the Year Award at the University of Maryland in 2007, Kos Ishii-Toshiba Award from ASME in 2011, Excellence in Research Award from ASME Computers and Information in Engineering Division in 2013, Distinguished Alumnus Award from Indian Institute of Technology, Roorkee in 2014, and ASME Design Automation Award in 2021. He was named "The 20 most influential professors in smart manufacturing" by Smart Manufacturing Magazine in June 2020. The Viterbi School of Engineering gave him Use-Inspired Research Award in 2021 for creating solutions that address the U.S. aerospace and defense industry's needs in the advanced manufacturing area. He has also received ten best paper awards at international conferences. He served as a mentor for the student team that won the 2017 ARIAC competition organized by the National Institute of Standards and Technology, and another team advised by him was a finalist for the 2017 Kuka Innovation Award.
Dr. Gupta's work has received significant attention from the media. His work has been covered by Economist, Forbes, Huffington Post, Baltimore Sun, LA Times, LA Business Journal, Audubon Magazine, IEEE Spectrum, Mechanical Engineering Magazine, Science News, and Smithsonian Magazine.
I am interested in developing human-centered automation solutions to increase human productivity, reduce human health risks, and enable innovation. Realizing human-centered automation requires robotics and automation technologies to reduce the physical human effort and enable humans to focus their effort on high-value tasks. It also requires augmenting human decision-making capabilities to reduce the probability of making mistakes, enabling creativity, and increasing decision-making speed.
I believe that robots can serve as smart assistants for humans. Many emerging robotics applications require multiple collaborating robots to operate under human supervision. To be useful in such applications, smart robotic assistants will need to (1) program themselves, (2) efficiently learn from the observed performance, (3) safely operate in the presence of uncertainty, (4) appropriately call for help during the execution of challenging tasks, and (5) effectively communicate with humans. We are making advances in physics-informed artificial intelligence to enable robots to exhibit smart behaviors. We are developing methods to automatically generate near-optimal trajectories in real-time to enable robots to program themselves from task descriptions. We are also developing self-supervised learning methods to equip robots with the ability to learn from observing the performance of previously executed tasks. We are developing methods for robots to operate safely in the presence of uncertainty by generating contingency-aware plans. We are developing computational methods for endowing robots with introspective capabilities so that they can seek help from humans on challenging tasks. If, during the execution, a contingency situation is detected, the system can issue appropriate alerts to humans and performs the necessary replanning. The system will assess its own confidence in executing a task, and if the risk appears to be high, it will seek human help. This approach enables the system to ensure human safety and enables it to quickly recover from errors. We are also exploring the use of augmented reality-based interfaces for enabling robots to elicit human guidance.
To augment human decision-making capabilities, I am interested in developing decision support systems that enable humans to make informed decisions in a timely manner for challenging applications. First, we are developing decision-making approaches that combine heuristic-aided discrete state-space search, non-linear optimization, and surrogate modeling to recommend informed decisions by the required decision-making deadlines. We are making advances in physics-informed artificial intelligence to enable the realization of decision support systems for applications that involve model uncertainty, complex physics, and fast decision-making speeds. Finally, we are developing a novel framework to combine model-based approaches and data-driven approaches in a consistent and unified manner. This enables us to exploit prior knowledge and augment missing components of models through safe and efficient learning.
Many applications will utilize multi-robot teams to perform environmental monitoring and information gathering tasks to provide situational awareness. Multi-robot systems can be particularly useful for dangerous missions in complex environments and reduce the risk to humans. Complex missions require the execution of spatially separated tasks by a team of robots. Planning strategies for such missions must consider the formation of effective coalitions among available assets and the assignment of tasks to robots with the goal of minimizing the expected mission completion time. The occurrence of unexpected situations that adversely interfere with the execution of the mission may require the execution of contingency tasks so that the originally planned tasks may proceed with minimal disruption. The planner must update the mission execution plan based on the probability of the potential contingency task to impact the mission tasks. It may either immediately incorporate the contingency task in the mission plan or defer its incorporation until the situation changes. We are developing a physics-informed artificial intelligence approach to proactively manage contingencies.
We anticipate that humans will be in supervisory roles to make decisions in critical situations and manage resources. At a high-level, humans will ensure that robots are working on tasks that align with the mission objectives as the mission progresses. In a mission with considerable uncertainty due to intermittent communications, degraded information flow, and failures, humans need to assess both the current and probable future states to make sound decisions. We are developing a simulation-based alert system that proactively notifies the human supervisor of possible undesirable events and serves as a decision support system for humans to make informed decisions.
Traditionally robots are used only on mass production applications. The manual programming of robots is economically not viable in high-mix applications. Therefore, many processing operations rely on manual labor in high-mix applications. The advent of human-safe robots is enabling robots to collaborate with humans on ergonomically challenging tasks and amplify human productivity. This enables robots to perform a large fraction of the task and only requires humans to perform the final touch-ups. In addition, the availability of 3D vision and force sensors enables robots to operate without custom fixtures and accommodate part and fixture variability. Smart robotic assistants powered by physics-informed artificial intelligence technology can program themselves from the high-level task descriptions and utilize sensor data to adapt their behaviors to deliver efficient and safe operational performance in high-mix applications. We are developing smart robotic assistants for a wide variety of manufacturing applications such as assembly, composite prepreg layup, kitting, finishing, inspection, and machine tending.
We are developing robotic cells to significantly expand additive manufacturing (AM) processes capabilities by enabling material deposition on non-planar geometries. Many composite parts have thin three-dimensional shell structures. Achieving the right fiber orientation is critical to the functioning of these parts. Printing them using conventional planar-layer AM processes leads to fibers being oriented in the plane of the layer. The capability to deposit the material using non-planar conformal layers can produce parts with improved material properties. Robots can be used to perform multi-resolution printing that finds the best trade-off between build speed and surface finish. Robots can also be used to realize supportless AM. AM is not expected to produce high-quality electronics in the near foreseeable future. Therefore, robots also enable the insertion of externally fabricated components such as sensors, actuators, and energy harvesting components during the AM process. Performing material deposition with robots requires solving many computational challenges. We are developing physics-informed artificial techniques needed for generating and executing robot trajectories to build high-quality parts using AM.
- 2022 The Engineers’ Council Distinguished Engineering Educator Achievement Award
- 2021 ASME Design Automation Award
- 2021 Viterbi School of Engineering, University of Southern California Use-Inspired Research Award
- 2021 Best Paper Award (Second Place), ASME Manufacturing Science and Engineering Conference
- 2021 Solid Modeling Association (SMA) Fellow
- 2020 Society of Manufacturing Engineers (SME) Fellow
- 2020 IEEE Fellow
- 2020 University of Southern California Maseeh Entrepreneurship Prize Competition, First Place
- 2020 Orange County Engineering Council Distinguished and Pioneer Educator Award
- 2019 First Place in 2019 SME Aerospace and Defense Manufacturing Conference Poster Challenge
- 2018 Best Paper Award in 2018 ASME Computers and Information in Engineering Conference
- 2018 Judge’s Choice Award at 2018 Reusable Abstractions of Manufacturing Processes Workshop
- 2018 Best Paper Award (Third Place) in 2018 ASME Manufacturing Science and Engineering Conference
- 2017 Finalist, Kuka Innovation Award
- 2017 First Place, NIST Agile Robotics for Industrial Automation Competition (ARIAC)
- 2017 ASME Computer Aided Product and Process Development Technical Committee's Best Paper Award
- 2014 Indian Institute of Technology, Roorkee Distinguished Alumnus Award
- 2013 ASME Computers and Information in Engineering Division's Excellence in Research Award
- 2013 Computer Aided Product and Process Development Technical Committee's Prakash Krishnaswami Best Paper Award, ASME Computers and Information in Engineering Conference
- 2012 Best Paper Award, ASME Computers and Information in Engineering Conference
- 2012 International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines Finalist for Best Paper
- 2012 Computer Aided Design Journal Most Cited Paper Award
- 2011 American Society for Mechanical Engineers (ASME) Design for Manufacturing and the Life Cycle Committee Kos Ishii-Toshiba Award
- 2010 ASME Mechanism and Robotics Conference Compliant Mechanism Application Award
- 2009 Bioinspiration & Biomimetics Highlights
- 2007 American Society of Mechanical Engineers (ASME) Fellow
- 2007 University of Maryland Finalist, Invention of the Year Award in Information Science Category
- 2007 University of Maryland Invention of the Year Award in Physical Science Category
- 2006 Science Spectrum Magazine Trailblazer Award
- 2006 ASME Computers and Information in Engineering Conference Best Paper Award
- 2003 University of Maryland Business Plan Competition
- 2002 National Academy of Engineering Selected to Attend Frontiers in Engineering Symposium
- 2001 Literati Club Highly Commended Award
- 2001 University of Maryland, Institute for Systems Research Outstanding Systems Engineering Faculty Award
- 2001 Society of Manufacturing Engineers Robert W. Galvin Outstanding Young Manufacturing Engineer Award
- 2001 National Science Foundation CAREER Award
- 2001 Presidential Early Career Award for Scientists and Engineers
- 2000 Office of Naval Research Young Investigator Award
- 1999 ASME Design for Manufacturing Conference Best Paper Award
- 1994 ASME Computers in Engineering Conference Best Paper Award in the area of Artificial Intelligence and Feature-Based Design and Manufacturing
- 1994 University of Maryland, Institute for Systems Research Outstanding Systems Engineering Graduate Student Award
- 1988 University of Roorkee Gold Medal for First Rank in Bachelor of Engineering in Mechanical Engineering
- 1988 University of Roorkee Gold Medal for the Best Engineering Design Project
- 1988 Science and Technology Entrepreneurship Park, First Prize, Roorkee Chapter Project Competition