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University of Pensylvania, USA
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Bio: Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor in Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. She holds secondary appointments in Computer and Information Science and Electrical and Systems Engineering and is a primary faculty of the GRASP laboratory. Before to joining Penn, she was a Postdoctoral Associate in the Interactive Robotics Group (part of CSAIL) at the Massachusetts Institute of Technology. She obtained a Ph.D. in Robotics, Control and Intelligent Systems from the Swiss Federal Institute of Technology in Lausanne (EPFL). Prior to this, she spent time as a Research Assistant in the Robotics and Mechatronics Institute at the German Aerospace Center (DLR) and at NYU Abu Dhabi. She holds a B.Sc. in Mechatronics from Monterrey Tech and M.Sc. in Automation and Robotics from TU Dortmund.
Her research focuses on developing tightly coupled learning, control and estimation algorithms to achieve fluid human-robot collaborative autonomy with safety, efficiency and robustness guarantees. This involves research at the intersection of control theory, machine learning, artificial intelligence, perception and biomechanics - with a physical human-robot interaction perspective. She has received several honors for her contributions to robotics, including being a finalist for the Georges Giralt PhD award, the KUKA innovation award and receiving the NSF CAREER award as well as best paper awards and nominations at major robotics venues such as RSS and TRO.
Talk title: Vision-Based End-to-End Navigation for Underwater Robots via Transferred Depth Features
Talk abstract: Underwater autonomy is fundamentally constrained by limited sensing modalities, making vision an increasingly attractive option for perception and navigation. However, severe underwater image degradation presents significant challenges for robust vision-based autonomy. This talk presents our recent efforts toward enabling reliable underwater navigation under such conditions.
We first introduce a physics-informed transfer framework for enhancing dense perception, with a particular focus on underwater depth estimation for navigation tasks. Building on these transferred depth features, we then develop a diffusion-based navigation policy that enables terrain following and obstacle avoidance directly from visual observations.
Bio: Ye received her B.S. degree from Shanghai Jiao Tong University, China, in 2008, her M.S. degree from the Technical University Berlin, Germany, in 2011, and her PhD degree from the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, advised by Prof. Colin Jones and Prof. Melanie Zeilinger from ETH in 2016. From 2016 to 2018, she was a postdoctoral researcher advised by Prof. Claire Tomlin at the University of California at Berkeley, USA. Ye received the Swiss National Science Foundation, Early Postdoc.Mobility fellowship in 2016-2018. She is a recipient of the ARC Discovery Early Career Researcher Award for 2022-2025. Ye is currently acting as the Deputy Head of the Control and Signal Processing (CSP) Group and the Manager of the CSP Laboratory at the University of Melbourne.
University of Melbourne
Indian Institute of Science
Talk title: Manifold-Constrained Learning and Spatio-Temporal Safety for Provably Stable Robot Manipulation
Talk abstract: Learning-based manipulation has demonstrated remarkable adaptability, yet integrating imitation learning and reinforcement learning with formal safety and stability guarantees remains a fundamental challenge. This talk presents a unified framework for certified robot learning in contact-rich and human-interactive environments. I introduce three complementary contributions. First, Certified Gaussian Manifold Sampling (C-GMS) constrains reinforcement learning exploration to a Lyapunov-certified manifold of stable impedance gain schedules for combined Dynamic Movement Primitives (DMPs) and Variable Impedance Control (VIC), guaranteeing stability and actuator feasibility by construction. Second, SafeDMPs integrates DMPs with Spatio-Temporal Tubes (STTs) to derive a closed-form, non-optimization-based safety controller that ensures collision avoidance against static and dynamic obstacles at high control frequencies. Third, STT-LfD treats demonstrations as data-driven safety specifications, learning time-varying intent envelopes using heteroscedastic Gaussian Processes and enforcing them through a model-free feedback law with formal guarantees. Together, these works illustrate a principled transition from imitation to certification, where learning and control are embedded within mathematically verifiable structures, enabling adaptable yet provably safe robot manipulation.
Bio: Ravi Prakash is an Assistant Professor at the Robert Bosch Centre for Cyber-Physical Systems at the Indian Institute of Science Bengaluru. Prior to this, he was a Postdoctoral Researcher in the Learning and Autonomous Control group at the Department of Cognitive Robotics, TU Delft. He earned his Ph.D. in Control & Automation from the Indian Institute of Technology Kanpur. His research has contributed towards skill learning and optimal control for intelligent robots. He is a recipient of the DAAD Postdoc Networking Fellowship for AI and Robotics, with funded research visits to the German Aerospace Center (DLR), Munich. His current research interests include learning complex manipulation policies from human demonstration/corrections, bimanual robot manipulation, task generalization in novel environments, and human-friendly safe compliant control. He has founded and directs the Human-interactive Robotics Lab at the Indian Institute of Science.
Talk title: Neural Dynamic Policy for Motor Skills Learning
Talk abstract: Policy optimization for robot skill transfer from limited demonstrations remains challenging due to insufficient exploration and poor generalization to unseen scenarios. In this paper, we propose a neural dynamic policy architecture which leverages deep networks to encode motor skills from demonstrations, expanding the parameter space beyond classical Dynamic Movement Primitives (DMP) and Gaussian Mixture Regression (GMM) approaches while enabling selective adaptation of high-level layers for new tasks. Extensive experiments on the LASA dataset demonstrate that SV-PINES converges significantly faster and achieves lower final costs compared to state-of-the-art baselines across via-point tracking, obstacle avoidance, and impedance learning tasks.
Bio: Yingbai Hu will join Hunan University as an Associate Professor in March 2026. He received his Ph.D. in Computer Science from the Technical University of Munich in 2022. From April 2022 to March 2026, he was a Research Postdoctoral Fellow at the Technical University of Munich and the Multi-scale Medical Robotics Center at The Chinese University of Hong Kong. He received the 2020 Best Paper Finalist award from IEEE Transactions on Mechatronics and the Best Conference Paper Award in Advanced Robotics at the IEEE International Conference on Advanced Robotics and Mechatronics (2020). His research interests include imitation learning, reinforcement learning, optimal control, and medical robotics.
Chinese University of Hong Kong
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Talk abstract: TBD
Bio: João Silvério is a senior researcher and group leader at the German Aerospace Center (DLR) in Munich, where he leads the Interactive Skill Learning group since 2022. He received a PhD in Robotics from the Italian Institute of Technology (IIT), Genoa, in 2017. From 2017 to 2019, he was a postdoctoral researcher at IIT, and from 2019 to 2022, he held a postdoctoral position at the Idiap Research Institute in Martigny, Switzerland. His research focuses on machine learning for robotics, with particular interest in learning for control, imitation learning, and reinforcement learning.
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