Abstract

Formal methods - mathematical techniques for describing systems, capturing requirements, and providing guarantees - have been used to synthesize robot control from high-level specification, and to verify robot behavior. Given the recent advances in robot learning and data-driven models, what role can, and should, formal methods play in advancing robotics? In this talk I will give a few examples for what we can do with formal methods, discuss their promise and challenges, and describe the synergies I see with data-driven approaches.

Bio

Hadas Kress-Gazit is the Geoffrey S.M. Hedrick Sr. Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University, and the Associate Dean for Diversity and Academic Affairs of Cornell Duffield College of Engineering. She received her Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania in 2008 and has been at Cornell since 2009. Her research focuses on formal methods for robotics and automation, and more specifically on high-level specifications and synthesis for robot control. Her group has explored different types of robotic systems including modular robots, soft robots, and swarms, and how formal methods can be used for human-robot interaction. She received an NSF CAREER award in 2010, a DARPA Young Faculty Award in 2012, Cornell Engineering’s Excellence in teaching award in 2013 and 2019, and excellence in research award in 2021. She is an IEEE Fellow and has served on DARPA’s Information Science and Technology study group (ISAT), as the program chair for Robotics: Science and Systems (RSS) 2018, the program chair for the International Conference on Robotics and Automation (ICRA) 2022, and the president of the RSS board (2019-2023), among other leadership positions in the robotics community.

Abstract

Programmable materials and structures hold great potential for various applications, such as robotics, biomedical devices, and civil structures. The rational design, physical realization, and validation of programmed behaviors in these systems play important roles in enabling functional devices. To encode desired mechanical functionality into structures, we propose a multi-material multi-objective topology optimization approach to inverse design composite structures that achieve complex target mechanical responses under large deformations. The multi-material framework simultaneously optimizes both the geometry, material heterogeneity, and architecture to achieve target behaviors and functionalities. A library of diverse designs is created, showcasing a wide range of precisely programmed nonlinear responses, such as multi-bulking and multi-plateau. In general, the properties of materials and structures typically remain fixed after being constructed. To enable reprogrammable behaviors, we develop a multi-physics topology optimization approach to discover magneto-active and temperature-active materials that achieve tunable buckling and switchable shape morphing, controlled by magnetic fields and temperature fields, respectively. The obtained systems exhibit one response under one stimulus and switch to a distinct response by applying another stimulus. To bridge the gap between simulation and fabrication, we explore multi-material manufacturing techniques, introduce advanced path generation methods, and develop direct ink writing (DIW) techniques to fabricate a suite of mechanical, magnetic, and thermal metamaterials and metastructures and experimentally validate their programmed behaviors. The excellent agreement among target, simulation, and experiment demonstrates that the proposed optimization-driven framework, when integrated with hybrid manufacturing techniques, has the potential to systematically design, inform, and create innovative multi-functional materials and structures for various engineering applications.

Bio

Dr. Xiaojia Shelly Zhang is a David C. Crawford Faculty Scholar and Associate Professor at the Department of Civil and Environmental Engineering and the Department of Mechanical Science and Engineering at the University of Illinois at Urbana Champaign (UIUC). She directs the MISSION (MuIti-functional Structures and Systems desIgn OptimizatioN) Laboratory. Dr. Zhang holds B.S. and M.S. degrees from UIUC and a Ph.D. degree from Georgia Tech. Her research explores multi-physics topology optimization, inverse design, stochastic learning algorithms, and additive manufacturing to develop multi-functional, sustainable, and resilient materials, structures, and robots for applications at different scales. She is the recipient of the National Science Foundation CAREER Award (2021), the ASME Journal of Applied Mechanics Award (2022), the DARPA Young Faculty Award (2022), the AFOSR Young Investigator Award (2023), the Leonardo da Vinci Award from ASCE (2024), the DARPA Director's Fellowship (2024), UIUC Campus Distinguished Promotion Award (2025), the Thomas J.R. Hughes Young Investigator Award from ASME (2025), the ASME Henry Hess Early Career Publication Award (2025), the Haftka Young Investigator Award from International Society for Structural and Multidisciplinary Optimization (2025), and Huajian Gao Young Investigator Medal from SES (2026). Dr. Zhang serves on the Executive Committee of the International Society of Structural and Multidisciplinary Optimization (ISSMO) and is a Review Editor for the Journal of Structural and Multidisciplinary Optimization and an Associate Editor for the Journal of Applied Mechanics.