Robotics, Vision, and Controls Talks

Abstract

At the nanoscale, building functional robotic systems requires rethinking fundamental concepts of actuation, sensing, and control. External fields like light or magnetism can be used to trigger motion, but certain biological environments may demand solutions that are autonomous, biocompatible, and responsive. In this lecture, I will present our latest work on DNA-based molecular nanorobots: programmable molecular nanostructures designed to navigate and interact within living cells and tissues. By precisely controlling enzyme placement and orientation on DNA scaffolds, we tune their propulsion and diffusion behavior across biological environments. Surface functionalization with antibodies allows us to engineer targeted interactions with cellular receptors and modulate mobility through complex barriers such as mucus. We make use of single-particle tracking techniques to directly observe nanorobot dynamics within biological environments and at the cellular interfaces, probing targeting accuracy interaction timescales. Moreover, we evaluate how ligand patterning modulates cell activation outcomes by measuring biomarker expression. This programmable nanorobotic platform brings together molecular design, motion control, and real-time sensing to enable new capabilities in bio-integrated robotics, with potential applications in diagnostics, drug delivery, and synthetic immunomodulation.

Bio

Prof. Tania Patiño is a biologist by training, and she obtained a PhD in Cell Biology from the Autonomous University of Barcelona, Spain. After her PhD, she joined the Institute of Bioengineering of Barcelona to develop enzyme-powered nanosystems for biomedical applications and in 2019 she obtained a Marie Sklodowska Curie fellowship at the University of Rome Tor Vergata, where she employed DNA nanotechnology to enhance nanorobot functionalities. Since 2022, she is assistant professor and group leader at the Department of Biomedical Engineering (TU/e) and the Institute of Complex Molecular Systems. Her group focuses on the development of molecular nanorobots that can interface cells in a programmable and controllable fashion for applications including cancer immunotherapy and regenerative medicine.

Abstract

We are entering a transformative era in health optimisation, marked by the continuous monitoring of our health and performance through health sensing technologies, such as wearables, nearables, smartwear, patches and implants. These technologies hold the potential to create personalised, self-optimised healthcare, allowing us to proactively manage our physical and mental well-being using validated data-driven decisions. By leveraging these technologies, our health system can be modernised to enable predictive diagnostics, optimised treatments and increased quality of care, ultimately reducing costs of healthcare.

Bio

Graham Kerr is a Professor in the School of Exercise and Nutrition Science at the Queensland University of Technology, Brisbane, Australia where he leads the Movement Neuroscience Research Program. This multidisciplinary research program has focused on understanding sensory and motor processes in movement control and how these are affected by injury, ageing and disease. This research has led to identification of biomarkers for optimising treatments, algorithms for predicting health outcomes and development of wearable technologies for monitoring and enhancing health and performance. His current research and training involvements include the ARC Training Centre in Transformative Health Sensing Technologies, ARC Training Centre in Joint Biomechanics, the Centre for Advanced Defence Research and Enterprise–OCE and the Defence Science Technology Group programs of Wearable Predictive Diagnostics and Human Integrated Sensor Systems.

Abstract

This talk will present research stemming from large prospective studies of older people and people with Parkinon’s disease that have provided understandings of neurophysiology and biomechanics underlying movement impairments that impact mobility and activities of daily living. This research has provided essential ground truth frameworks for developing wearable sensor and active technologies to monitor and enhance performance. It has also provided multimodal databases utilised for development of predictive diagnostics.

Bio

Carmel Majidi is the Clarence H. Adamson Professor of Mechanical Engineering at Carnegie Mellon University, where he leads the Soft Machines Lab. His lab is dedicated to the discovery of novel material architectures that allow machines and electronics to be soft, elastically deformable, and biomechanically compatible. Currently, his research is focused on modeling, design, and control of soft robotic systems as well as the developoment of multifunctional materials that exhibit unique combinations of mechanical, electrical, and thermal properties and can function as “artificial” skin, nervous tissue, and muscle.

Bio

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University, the William George and Ida Mary Hoover Faculty Fellow, and a co-founder of Physical Intelligence (Pi). Her research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has pioneered end-to-end deep learning methods for vision-based robotic manipulation, meta-learning algorithms for few-shot learning, and approaches for scaling robot learning to broad datasets. Her research has been recognized by awards such as the Sloan Fellowship, the IEEE RAS Early Academic Career Award, and the ACM doctoral dissertation award, and has been covered by various media outlets including the New York Times, Wired, and Bloomberg. Prior to joining Stanford, she received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley.

Abstract

General purpose robots are an important technical challenge that would be enabling for agriculture, ocean health, space missions, disaster recovery, personal medical care, and many other uses. The proliferation of quadrotor and, now, quadrupedal robots are beginning to show the value of general utility, but still fail to achieve the mobility and manipulation dexterity, as well as efficiency and operational lifetime of animals. Animals have far better performance, but they are presently far too complicated to build synthetically. Nature balances the trade off between architectural complexity and energetic cost of construction better than we do. I will present context to complex material systems and autonomy, the concept of Autonomous Material Systems, as well as synthetic and living approaches towards this hierarchy and interconnectivity. The results indicate that distributing sensing, actuation, energy, and communication in robots has great advantages, but is likely insufficient without living materials. In this talk, I will focus on advances in the use of aqueous redox flow batteries as hydraulic fluids for high energy density underwater robots, arrays of powerful microscale combustion engines, and a bioelectronic interface with mycelia that can eventually be used to control these systems.

Bio

Robert Shepherd is the John F. Carr professor of engineering at Cornell University in the Sibley School of Mechanical & Aerospace Engineering. He received his B.S. (Material Science & Engineering), Ph.D. (Material Science & Engineering), and M.B.A. from the University of Illinois. At Cornell, he runs the Organic Robotics Lab (ORL: http://orl.mae.cornell.edu), that focuses on using methods of invention, including bioinspired design approaches, in combination with material science and mechanical design to improve machine function and autonomy. We rely on new and established synthetic approaches for soft material composites that create new design opportunities in the field of robotics. He is the recipient of an Air Force Office of Scientific Research Young Investigator Award, an Office of Naval Research Young Investigator Award, is a Senior Member of the National Academy of Inventors, and his lab's work has been featured in popular media outlets such as the BBC, Discovery Channel, and PBS's NOVA documentary series. He is an advisor to the American Bionics Project (americanbionics.org) which aims to make wheelchairs obsolete. He is also the co-founder of the Organic Robotics Corporation (DBA LLume; http://llume.io) which aims to digitally record the tactile interactions of humans and machines with their environment. He is also the co-founder of MAV-Unlimited, Inc. (https://mav-unlimited.com), which seeks to make mass customization of consumer goods possible via high throughput 3D printing.

Abstract

State-of-the-art autonomy methods remain fragmented with controllers, sensor fusion pipelines, and learning algorithms typically tailored to narrow robot morphologies and operating regimes. This specialization has historically been necessary to achieve operational results, but inevitably limits generalization and slows the pace of innovation. A common blueprint for autonomy is necessary. This talk outlines a vision toward Unified Resilient Autonomy that is applicable across diverse robot configurations, whether flying, aquatic, or ground systems. By pursuing a common autonomy architecture and leveraging the lessons learned from its broad evaluation in extreme conditions, we demonstrate resilient functionality that transfers across embodiments. The discussion will highlight both the underlying methods that enable this unification as well as concrete results from field testing in unconventional environments - such as subterranean settings, ship ballast tanks, and submarine bunkers.

Bio

Prof. Dr. Kostas Alexis is a Professor at the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU), head of the Autonomous Robot Lab, and Director of the Norwegian Centre for Embodied AI. Together with his team, he conducts research on resilient robotic autonomy, exploring how autonomous systems can operate in high-risk, uncertain environments by presenting resourcefulness, robustness, and redundancy. Focusing on enhancing and safeguarding the autonomy capabilities of robotic systems, his research cuts across model-based optimization for control, sensor fusion, path planning, and learning algorithms for navigation. Prof. Alexis has served as Principal Investigator in major international grants both in Europe and the US, and was the PI and team lead of Team CERBERUS, winners of the DARPA Subterranean Challenge.