קולוקוויום בביה"ס למדעי המחשב - Computational Challenges and Algorithms in Planning for Robotic Systems

Abstract:

In recent years, robots have played an active role in everyday life: medical robots assist in complex surgeries, low-cost commercial robots clean houses and fleets of robots are used to efficiently manage warehouses. A key challenge in these systems is motion planning, where we are interested in planning a collision-free path for a robot in an environment cluttered with obstacles. While the general problem has been studied for several decades now, these new applications introduce an abundance of new challenges. 

In this talk I will describe some of these challenges as well as algorithms developed to address them. I will overview general challenges such as compression and graph-search algorithms in the context of motion planning. I will show why traditional Computer Science tools are ill-suited for these problems and introduce alternative algorithms that leverage the unique characteristics of robot motion planning. In addition, I will describe domains-specific challenges such as those that arise when planning for assistive robots and for humanoid robots and overview algorithms tailored for these specific domains.

Bio:

Oren Salzman completed a PhD in the School of Computer Science at Tel Aviv University under the supervision of Prof. Dan Halperin. He is currently a postdoctoral researcher at Carnegie Mellon University working with Siddhartha Srinivasa and Maxim Likhachev. His research focuses on revisiting classical computer science algorithms, tools and paradigms to address the computational challenges that arise when planning motions for real-world robots. Combining techniques from diverse domains such as computational geometry, graph theory and machine learning, he strives to provide efficient algorithms with rigorous analysis for robot systems with many degrees of freedom moving in tight quarters. He earned his BSc with honors from the Technion and his MSc with honors from Tel Aviv University.