IEEE Lonestar RAS

This presentation is by IEEE Fellow and RAS Distinguished Lecturer Dr. Lydia E. Kavraki.    The meeting, on the UTSA University campus, is open to everyone and IEEE student members from all San Antonio universities are especially invited to attend.  A dinner will be provided for a cost of $25 for IEEE members, $30 for non-IEEE members and no charge for students. A virtual option will be available - see the vTools event. Abstract Over the past sixty years, robots have progressed from isolated industrial artifacts to interactive, semi-autonomous agents increasingly embedded in next-generation factories, hospitals, and homes. Yet, realizing robots that collaborate safely, robustly, and transparently with people and with one another continues to pose deep theoretical and practical challenges. This talk will trace the evolution of the sampling-based paradigm in robot motion planning with emphasis on recent developments and highlight its central role in enabling reasoning under uncertainty, long-horizon autonomy, and human-centered collaboration. It will discuss how motion planning confronts the fundamental tension between the complexity of high-dimensional robotic systems and the stringent constraints imposed by physical embodiment, including dynamics, sensing, and real-world interaction. Building on this foundation, the talk will explore the relationship between motion planning and higher-level decision-making, where specifications can define what a robot must accomplish rather than how it should accomplish it, and the frameworks required to support more capable and reliable robots that serve people in shared environments. Biography: Lydia E. Kavraki is the Kenneth and Audrey Kennedy Professor of Computing and Professor of Computer Science and Bioengineering at Rice University. She is also the Director of the Ken Kennedy Institute for AI and Computing. Kavraki’s research develops the AI and the algorithmics needed to connect the digital to the physical world. She has two main areas of application for her research. In robotics, she develops methodologies for motion planning, machine learning methods for reasoning under uncertainty, and multimodal frameworks for instructing robots and collaborating with them. In computational biomedicine, she develops AI methods to understand biomolecular interactions and aid the design of new therapeutics. Kavraki is a member of the National Academy of Engineering, the National Academy of Sciences, the National Academy of Medicine, and the American Academy of Arts and Sciences. She is the recipient of the IEEE Robotics and Automation Society Pioneer Award and the IEEE Frances E. Allen Medal. More information about her work can be found at https://profiles.rice.edu/faculty/lydia-e-kavraki

Please join us for our March meeting, which will bring in (virtually from Finland) a distinguished lecturer for the Robotics & Automation Society.  We'll have a local group at Trinity University, or you can join us via zoom from the comfort of your office or home.  Virtual Information will be available closer to the event. Steven M. LaValle Professor of Robotics and Virtual Reality University of Oulu & University of Illinois 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. Bio: 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. Logistics: Registration: vTools Events link will be provided closer to the event. Local - Trinity University, Center for the Sciences and Innovation, Rm 205.  Park at Alamo Stadium and walk over.  Lunch will be provided for registrants. Hybrid - Zoom link will be posted closer to the event.

Due to logistics considerations this meeting will be in-person only. The meeting will begin with a tour followed by a presentation.   The tour will include a walk-through of the Plus One Customer Experience Center showing a robotic depalletization cell (DepalOne), the lineup of robot grippers, and a live demo of  worldwide supervised autonomy infrastructure named Yonder. It will conclude with a lab unit that is the Plus One flagship product, robotic parcel induction (InductOne).   After the tour we will meet in a conference room for pizza (no charge), a brief Chapter business meeting and a presentation on the work Plus One is doing in AI-enabled robotic guidance and target identification software .