To address these challenges, this lecture presents a framework for contact-rich robotic assembly in construction that integrates perception, planning, and control. Within this framework, three complementary approaches are examined: adaptive control to compensate for model mismatch and disturbances; learned sensorimotor policies to handle complex, tight-tolerance interactions for which effective control strategies are difficult to formulate analytically; and a hybrid approach that combines learned policies with adaptive control to improve robustness and enable zero-shot sim-to-real transfer without real-world demonstrations for policy training. Through construction-scale case studies, the lecture highlights key methods, experimental findings, and current limitations of these approaches. It concludes by discussing open challenges and future directions for generalizing across material systems and component geometries, transferring skills between tasks, and extending these methods to longer assembly sequences in construction.

Bio: Dr. Arash Adel is an Assistant Professor at the School of Architecture, a Core Faculty of Princeton Robotics, and an Associated Faculty of the Department of Computer Science at Princeton University. He is the founder and director of Adel Research Group (ARG). ARG conducts interdisciplinary research at the intersection of robotics, artificial intelligence, and computational design; the research contributes to resilient, sustainable, and low-carbon construction outlooks and achievements. At the core of ARG’s comprehensive research is investigating human–robot collaborative processes, which tackle fundamental questions related to the future of the design and construction industries and their potential to have a broader impact on inclusive and equitable building culture. Prior to joining Princeton University in 2023, Adel was an Assistant Professor of Architecture at the University of Michigan’s Taubman College of Architecture and Urban Planning. Adel received his Doctorate in Architecture from the Swiss Federal Institute of Technology (ETH) and his Master’s in Architecture from Harvard University.

Bio: Enric Boix-Adsera is an assistant professor of statistics and data science. His parents are originally from Barcelona, and he grew up mostly in Princeton, New Jersey. Before coming to Wharton, he earned a doctorate in electrical engineering and computer science from MIT.

Boix-Adsera studies the learning processes of artificial intelligence models, such as ChatGPT, with the goal of improving their efficiency and reliability.

 For CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

Bio:  Hal Daumé III is a professor of computer science with appointments in the Maryland Language Science Center and the University of Maryland Institute for Advanced Computer Studies, where he is also the director of TRAILS. In addition to fairness and natural language processing, his research focuses on understanding computational properties of learning and language as well as trustworthy AI.

For CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

Bio: Jeremy D. Brown, the John C. Malone Assistant Professor in the Department of Mechanical Engineering, explores the interface between humans and robotics, with a specific focus on medical applications and haptic feedback.

Brown’s research sits at the intersection of engineering, biomechatronics, medicine, and psychophysics. He develops novel haptic interfaces for upper-limb prosthetics and minimally invasive surgical robotics. Applications of this research include giving amputees a sense of touch through their prostheses ,and helping surgeons use surgical robots to improve their accuracy and precision when performing delicate procedures.

Bio: Tapomayukh "Tapo" Bhattacharjee is an Assistant Professor in the Department of Computer Science at Cornell University where he directs the EmPRISE Lab (https://emprise.cs.cornell.edu/). He completed his Ph.D. in Robotics from Georgia Institute of Technology and was an NIH Ruth L. Kirschstein NRSA postdoctoral research associate in Computer Science & Engineering at the University of Washington. His primary research interests are in the area of physical robot caregiving and physical human-robot interaction. He is the recipient of TRI Young Faculty Researcher Award'24, NSF CAREER Award'23, AFCEA 40 under 40 Award'22, and his work has won Best Systems Paper Award at HRI’26, Best Paper Award at RSS’25, Best Paper and Student Paper Award Finalist and Best HRI Paper Award Finalist at ICRA’25, Best Systems Paper Award Finalist at HRI'24, Best Demo Award at HRI'24, Best RoboCup Paper Award at IROS’22, Best Paper Award Finalist and ABB Best Student Paper Award Finalist at IROS’22, Best Technical Advances Paper Award at HRI'19, and Best Demonstration Award at NeurIPS’18. His work has also been featured in many media outlets including the BBC, Reuters, New York Times, IEEE Spectrum, and GeekWire and his robot-assisted feeding work was selected to be one of the best interactive designs of 2019 by Fast Company. 

Bio: Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania, with secondary appointments in Computer and Information Science and Electrical and Systems Engineering. She is also a core faculty member of the GRASP Lab. Before joining Penn, she was a Postdoctoral Associate in the Interactive Robotics Group at the Massachusetts Institute of Technology (MIT). She earned her Ph.D. in Robotics, Control, and Intelligent Systems from the Swiss Federal Institute of Technology in Lausanne (EPFL) and previously conducted research at the Robotics and Mechatronics Institute of the German Aerospace Center (DLR) and at NYU Abu Dhabi. She holds a B.Sc. in Mechatronics Engineering from Tecnológico de Monterrey and an M.Sc. in Automation and Robotics from TU Dortmund. Her research focuses on developing integrated learning, control, and estimation algorithms that enable fluid human-robot training, co-existence and collaboration with formal guarantees of safety, efficiency, and robustness. Her work bridges control theory, machine learning, AI, perception, and biomechanics through the lens of physical human-robot interaction. She is a recipient of a 2025 NSF CAREER Award, a finalist for the 2020 Georges Giralt Best Ph.D. and 2017 KUKA Innovation Awards, and has received multiple best paper honors at leading robotics venues such as ICRA, RSS and T-RO.

Bio: The Ghosh group engages in cross-disciplinary collaborations and welcomes students from diverse academic backgrounds. Most projects focus on addressing challenges in fundamental physics and astrophysics using computational and AI/ML tools, making the group a natural fit for students with strong skills or interests in these areas. We develop methods to automate theoretical physics calculations using reinforcement learning and LLM agents, enabling rapid testing of new theories. We also work on simulation, experimental design and high-dimensional statistical inference techniques powered by AI to accelerate scientific discovery. Data analysis problems at the scale of the Large Hadron Collider or multi-messenger astronomy often demand rapid decision-making, and we design efficient AI algorithms that can be deployed on fast hardware to meet these challenges.

For CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

Bio: TBA

For CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.