Magnus Frodigh

Head of Ericsson Research

Abstract:The coming 5G networks will be a platform for a large number of communication applications. Higher data rates, shorter latencies, quality of service and security will accommodate many more demands than just voice communication and internet access. With a cloud moving towards the edge, compute and storage resources will move closer to the end user – or maybe we should say “end machine”, as we will se 5G coming into play in many industrial applications where for instance robot control loops can take place wirelessly over 5G

Alexandre Proutiere

Professor in the Department of Automatic Control, KTH Royal Institute of Technology

 Abstract: Real-world Reinforcement Learning (RL) problems often concern dynamical systems with large state and action spaces. For such systems, it is essential to  identify and exploit any structural property of the system dynamics and reward function. Leveraging a known structure in turn may help minimizing the exploration of sub-optimal (state, action) pairs, and speed up the learning process. Modern RL algorithms actually implicitly impose some structure either in the model parameters (transition probabilities and reward function) or directly in the Q-function. Despite the successes of these recent algorithms, our understanding of structured RL problems remains limited. In this talk, we provide an overview of both theoretical and practical methods developed recently to tackle large-scale structured RL problems, and list important remaining challenges in the field.

Magnus Egerstedt

Executive Director for the Institute for Robotics and Intelligent Machines at the Georgia Institute of Technology and a Professor and the Julian T. Hightower Chair in Systems and Controls in the School of Electrical and Computer Engineering

Abstract: By now, we have a fairly good understanding of how to design coordinated control strategies for making teams of mobile robots achieve geometric objectives in a distributed manner, such as assembling shapes or covering areas. But, the mapping from high-level tasks to these objectives is not particularly well understood. In this talk, we investigate this topic in the context of long-range autonomy, i.e., we consider teams of robots, deployed in an environment over a sustained period of time, that can be recruited to perform a number of different tasks in a distributed, safe, and provably correct manner. This development will involve the composition of multiple barrier certificates for encoding the tasks and safety constraints, as well as a detour into ecology as a way of understanding how persistent environmental monitoring, as a special instantiation of the long-range autonomy concept, can be achieved by studying animals with low-energy life-styles, such as the three-toed sloth.