Scaling-Up Planning for Long-Lived Autonomous Robots

Dr. Khen Elimelech - Rice University

30 January 2024, 14:00 
zoom & Room 206 
Scaling-Up Planning for Long-Lived Autonomous Robots

Join us with Zoom


How can a search-and-rescue drone locate a person trapped in a demolished building it has never seen before? How can an autonomous car find its path to a desired destination? How can our new companion robot figure out the steps to clean up the bedroom? Answering all these questions requires long-horizon online planning. Automation of the planning process is the basis for enabling robot autonomy, which can benefit countless applications. Unfortunately, despite its fundamental importance, planning for realistic tasks remains a great challenge. As planning generally requires predictive search, its complexity grows exponentially. This often renders planning algorithms intractable for problems characterized by complex logical specifications, high-Degree-of-Freedom control, uncertainty, and non-monotonic solutions. Addressing this concern, Dr. Elimelech’s research goal is to develop paradigms for robust solution of high-scale robotic problems. His work is characterized by fundamental algorithmic and theoretical contributions, which can be integrated with existing planners to increase their scalability. This talk will cover two of his recent research projects. The first project contributes to the efficient solution of “active Simultaneous Localization and Mapping (SLAM),” the fundamental problem behind long-duration mobile-robot navigation, which involves decision making under uncertainty in high-dimensional state spaces. As this work suggests, such problems can be automatically simplified, using predictive modification or sparsification of the robot’s “belief” (uncertain estimate of the world state). This approach is supported by a theoretical framework allowing us to derive formal guarantees for the potential optimality loss. The second project supports long-lived autonomous robots by providing a theoretical and computational framework for planning experience reuse, allowing them to improve their cognitive abilities throughout their operation, without human intervention. This novel framework establishes techniques for online, automatic, lifelong encoding of individual experiences as generalizable “abstract skills,” which can later be adapted for new contexts in real-time. This approach was demonstrated for robot manipulators performing object assembly, formulated as “task and motion planning.”



Dr. Khen Elimelech is a postdoctoral researcher and an instructor in the Department of Computer Science at Rice University, where he works with Professors Lydia Kavraki and Moshe Vardi. He also serves as the President of the Rice University Postdoctoral Association (RPA). Before joining Rice, he earned a B.Sc. in Applied Mathematics from Bar-Ilan University, and a Ph.D. from the Robotics and Autonomous Systems Program at the Technion– Israel Institute of Technology, under the supervision of Professor Vadim Indelman. For his Ph.D. thesis he won the national “Outstanding Ph.D. Research Award,” on behalf of the Israeli Smart Transportation Research Center (ISTRC). For his postdoctoral work, he was honored as a finalist for the “Outstanding Postdoctoral Research Award,” on behalf of Rice’s School of Engineering. During his studies he was also recognized amongst the prestigious cohort of "Robotics: Science and Systems (R:SS) Pioneers” and amongst the “Top A.I. Student Ambassadors” by Intel. His research focuses on developing algorithms and theory allowing autonomous robots to robustly and efficiently perform cognitive tasks, such as planning, decision making, generalizing from experiences, and overcoming uncertainty.



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