Work towards PhD degree under the supervision of Prof. Erez Karps and Prof. Emeritus Per-Olof Gutman
When: 4.11.2021 at 11:00
Abstract: Robots operate in the real world, which is hybrid, i.e. comprised of continuous and discrete properties, uncertain, constrained, non-linear, and often cooperation or at least synchronization with other agents, human and robotic is required. Moreover, usually for an agent to reach its desired goal, a long time horizon is required, which accumulates errors and makes it dimensionally impossible to discretize the problem. Each of these separately poses a major challenge for autonomous behavior. Robots must be able to come up with long-term plans in the face of these challenges in order to reach autonomy. Various communities have addressed these problems; e.g., the control, automated planning, machine learning, and robotics communities, each with its own merits and weaknesses. In this work, we attempt to bridge the gap between these communities and present a unified method leveraging accurate short-term control strategy, long-term abstract planning methods, and deep neural networks tailored on the fly. Our method can handle long, continuous horizons, allowing for concurrency and synchronization, incorporation of accurate non-linear dynamic models, while balancing between expensive accurate computations and “simple” abstract computations.
You can watch the seminar here