IEEE Latin American Robotics Symposium (LARS) Program

Abstract

The field of robotics is wildly exciting and rapidly gaining worldwide attention, yet it is often an enigma in terms of its scope and scientific foundations. Robotics involves the design, programming, and analysis of movable machines that accomplish useful work through sensing and manipulation of the surrounding world. Throughout the decades it has been varyingly viewed as an application field of more mature disciplines such as computer science (AI, algorithms, machine learning) and mechanical engineering (kinematics, dynamics, nonlinear control). This talk will argue that robotics has its own unique and growing scientific core, with deep questions and modeling challenges that should inspire new directions in computer science, engineering, and even pure mathematics.

We will start with a Turing-inspired way to view robotics or embodied AI, leading to some of our recent results that characterize minimally sufficient amounts of sensing, actuation, or computation that are required to solve physical tasks. Questions addressed include: How are learning, planning, and control related? How do we know when it is impossible to solve a task? When will learning fail, even with an infinite amount of data? Does a universal action sequence exist that would cause a robot to solve any possible task without modification? How important are semantics and representations? Interspersed throughout the talk will be results and perspective from my research in the field over three decades, produced with many inspiring students, mentors, and collaborators.

Biography

Steven M. LaValle has been Professor of Computer Science and Engineering, in Robotics and Virtual Reality, at the University of Oulu, Finland since 2018. Since 2001, he has been a professor in the Department of Computer Science at the University of Illinois. He has also held positions at Stanford University and Iowa State University. His research interests include robotics, virtual reality, sensor fusion, planning algorithms, computational geometry, and control theory. In research, he is mostly known for his introduction of the Rapidly exploring Random Tree (RRT) algorithm, which is widely used in robotics and other engineering fields. He also authored the books Planning Algorithms, Sensing and Filtering, and Virtual Reality. He currently leads an Advanced Grant project from the European Research Council on the Foundations of Perception Engineering.

With regard to industry, he was an early founder and chief scientist of Oculus VR, acquired by Facebook for $3 billion in 2014, where he developed patented tracking technology for consumer virtual reality and led a team of perceptual psychologists to provide principled approaches to virtual reality system calibration, health and safety, and the design of comfortable user experiences. From 2016 to 2017, he was a Vice President and Chief Scientist of VR/AR/MR at Huawei Technologies, where he was a leader in mobile product development on a global scale. He has worked as an angel investor and adviser to startups in robotics and virtual reality.

Biography

Rafael Fierro is a Professor in the Department of Electrical and Computer Engineering at the University of New Mexico (UNM), a position he has held since 2007. He earned his MSc degree in Control Engineering from the University of Bradford, England, in 1990, and subsequently completed his PhD in Electrical Engineering at the University of Texas at Arlington in 1997. Prior to joining UNM, Dr. Fierro conducted postdoctoral research at the General Robotics, Automation, Sensing & Perception (GRASP) Laboratory at the University of Pennsylvania and later held a faculty position in the Department of Electrical and Computer Engineering at Oklahoma State University.

His current research interests include cyber-physical systems and robotic networks, coordination and planning in heterogeneous multi-agent systems, uncrewed aerial vehicles (UAVs), and advanced collaborative robot manipulation for on-orbit servicing. Dr. Fierro’s research has received support from the National Science Foundation (NSF), U.S. Army Research Laboratory (ARL), Air Force Research Laboratory (AFRL), Department of Energy (DOE), Sandia National Laboratories, and the Breakthrough Foundation.

He serves as the director of the AFRL-UNM Agile Manufacturing Center and the Multi-Agent, Robotics, and Heterogeneous Systems (MARHES) Laboratory. Dr. Fierro has received several honors, including a Fulbright Scholarship, a National Science Foundation CAREER Award, and the 2008 International Society of Automation (ISA) Transactions Best Paper Award. Additionally, he has contributed to the academic community by serving as an associate editor for the Journal of Intelligent & Robotic Systems, IEEE Control Systems Magazine, IEEE Transactions on Control of Network Systems (T-CNS), and IEEE Transactions on Automation Science and Engineering (T-ASE). He is a Senior Member of the IEEE.

Abstract

Several essential domains of robotics, such as surveillance, planetary exploration, oceanic monitoring, and search and rescue, require filtering and planning for robots in scenarios where communication, sensing, and uncertainty modeling are challenging. In this talk, I will discuss our progress and experiments with collaborators in addressing these issues in three directions: 1) new limited-communication coordination and learning approaches that can work in extremely low-bandwidth conditions; 2) a framework for predicting the near-term behavior of a reactive agent in scenarios where observations are sparse or intermittent; and 3) approaches to localize and navigate using features of naturally occurring scalar fields.

Biography

Dr. Leonardo Bobadilla is currently an Associate Professor at the Knight Foundation School of Computing and Information Sciences (KFSCIS), College of Engineering and Computing at Florida International University (FIU). He received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2013. He is interested in understanding the information requirements for solving fundamental robotics tasks such as navigation, patrolling, tracking, and motion safety. He has deployed test beds that monitor and control many mobile units requiring minimal sensing, actuation, and computation. He has published over 80 peer-reviewed journal articles and conference papers in Robotics, Control, and Oceanic Engineering. His research articles have appeared in prestigious journals such as IEEE Transactions of Automation Science and Engineering, IEEE Journal of Oceanic Engineering, IEEE Robotics and Automation Letters, ACM Transactions on Sensor Networks, and Journal of Intelligent and Robotic Systems, and in top conferences such as ICRA (IEEE International Conference on Robotics and Automation), IROS (IEEE/RSJ International Conference on Intelligent Robots and Systems), and RSS (The Robotics: Science and Systems). The ARL, ARPA-E, DoD, NSF, ONR, DHS, FDEP, and the Ware Foundation have sponsored his research.

Abstract

For decades, we have envisioned robots that can move, adapt, and collaborate with humans as fluidly as we do with each other. Yet even the most advanced systems struggle to achieve what I call fluid physical interaction: a regime where motion, force, and intent predictions are continuously co-regulated through constraint-aware estimation, learning, and control; enabling safe, adaptive, and seamless exchanges of energy and information. Achieving this requires robots that are stable yet flexible, adaptive yet predictable, and compliant yet purposeful. In this talk, I will show how a dynamical-systems perspective provides a unifying foundation for realizing fluid physical interaction. By shaping robot behavior through stable vector fields, viability manifolds, and adaptive dynamics models, we can generate motion policies that remain reactive, safe, and consistent even under uncertainty. I will present recent results that demonstrate how this framework enables robots to generalize complex behaviors from minimal demonstrations, adapt online to changes in their dynamics and environment, avoid obstacles in real time, and maintain long-horizon safety during physical collaboration. I will also discuss new efforts that combine dynamical-systems principles with visuomotor learning to achieve stable, compliant, and force-aware behavior from simulation-guided data. Together, these developments move us closer to embodied, adaptive, and compliant robot intelligence that can truly flow with humans and the environments they inhabit.

Biography

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 a core faculty member of the GRASP Lab. Her research integrates learning, control, and estimation to enable safe, adaptive, and fluid human–robot interaction and collaboration. Her work bridges control theory, machine learning, perception, and biomechanics, with an emphasis on formal guarantees for robot behavior in dynamic, human-centered environments. Before joining Penn, she was a Postdoctoral Associate in MIT’s CSAIL. She earned her Ph.D. in Robotics, Control, and Intelligent Systems from EPFL and conducted research at the German Aerospace Center (DLR) and 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. She received a 2025 NSF CAREER Award and was a finalist for the 2020 Georges Giralt Ph.D. Award and the 2017 KUKA Innovation Award. Her publications have received multiple best paper honors at ICRA, CASE, RSS, and IEEE Transactions on Robotics. She chairs the IEEE RAS Women in Engineering Committee, leading global efforts to recognize and celebrate women in robotics.

Abstract

In this work we address the challenge of ensuring stability in bipedal walking robots and exoskeletons. Numerous robot and control models rely on bioinspired models, such as central pattern generators (CPGs), which can be either symmetrical or asymmetrical.

The bioinspiration for biped gait is not the only way. We explore the feasibility of real-time implementation of analytical criteria, such as the Zero Moment Point, the Step Capturability or the Predicted Step Viability algorithm (PSV).

The PSV is a robust, yet complex multi-step optimization criterion for planning future steps in bipedal gait. To overcome the high computational cost of the PSV algorithm, we apply machine learning techniques to guarantee biped robot stability using the PSV results that can be computed data is balanced and sufficient points are used. Notably, by leveraging classifiers, we achieved rapid computation of results in less than 1 ms, with minimal computational cost in embedded systems.

Short biography

I am an Associate Professor in the Mechatronics Department at the Escola Politécnica da Universidade de São Paulo (USP), which I joined in 2010 and where I founded the Biomechatronics Lab. Since 2024, I have been Director of the Center of Assistive Technologies for Activities of Daily Life (CTECVIDA), a collaborative R&D hub that brings together universities, hospitals, technological institutes, government bodies, companies, and NGOs to create assistive mobility solutions with input from all stakeholders.

My research focuses on the neural mechanisms that drive human movement and on applying these insights to biomimetic robots, including bipeds and exoskeletons. I also study how sleep deprivation affects motor control. Over the years, I have contributed more than 200 works, including journal articles, conference papers, books, patents, standards, and measuring and assistive devices.

I earned my Engineering degree at the Universidad Politécnica de Valencia (Spain) and my PhD at the Universiteit Twente (The Netherlands). I then pursued postdoctoral research at Katholieke Universiteit Leuven (Belgium) and held a Ramón y Cajal fellowship at CSIC (Spain) before joining USP in Brazil.

I joined IEEE as a student member in 1990, but I have been more active since 2012. I am Senior Editor of the IEEE Journal of Translational Engineering in Health and Medicine and of the IEEE Transactions on Medical Robotics and Bionics, and I currently serve as EMBS R9 AdCom representative. In recent years, I have been an IEEE Distinguished Lecturer and Theme Editor of the IEEE EMBC, chaired the 5th IEEE RAS & EMBS BioRob in São Paulo, the first edition held in Latin America, and served as Chair and Vice-Chair of the EMBS Biorobotics Technical Committee as well as of the local EMBS and RAS chapters.