Title: Representations and Models for Collaboratively Intelligent Robots
Speaker: Subramanian Ramamoorthy
Abstract: We are motivated by the problem of building autonomous robots that are able to work collaboratively with other agents, such as human co-workers. One attribute of such an autonomous system is the ability to make predictions about the actions and intentions of other agents in a dynamic environment, and to adapt its own actions accordingly. After an initial overview of robotic systems we have developed in my lab, including mobile robots that can navigate effectively in crowded spaces and humanoid robots that can cooperate in assembly tasks, I will present recent results from my group addressing three basic questions: (a) how to efficiently capture the hierarchical nature of activities, (b) how to rapidly estimate latent factors, such as hidden goals and intent, (c) how to optimally make sequential decisions in interactive settings, with incomplete prior knowledge of the profiles of other agents.
Firstly, I will describe a procedure for topological trajectory classification, using the concept of persistent homology, which enables unsupervised extraction of certain kinds of relational concepts in motion data. One use of this representation is in devising a multi-scale version of Bayesian recursive estimation. In general, such recursive estimation over latent factors can be unacceptably expensive for our application scenarios. I will describe how in many robotics problems, one can structure the problem as one of counterfactual reasoning over a smaller set of simulation models. While this basic idea is intuitive, it can also be made formal and applied to situations involving multi-agent decision-making. By conceptualizing the interaction in terms of “policy types” in an incomplete information game, we obtain a learning algorithm that combines the benefits of Harsanyi’s notion of types and Bellman’s notion of optimality in sequential decisions. Based on results from initial human-machine experiments, I will show how this algorithm achieves a better rate of coordination than alternate multi-agent learning algorithms.
Bio: Dr. Subramanian Ramamoorthy is a Reader (Associate Professor) in the School of Informatics, University of Edinburgh, where he has been on the faculty since 2007. He is a Coordinator of the EPSRC Robotarium Research Facility, and Executive Committee Member for the Centre for Doctoral Training in Robotics and Autonomous Systems. He received his PhD in Electrical and Computer Engineering from The University of Texas at Austin in 2007. He is an elected Member of the Young Academy of Scotland at the Royal Society of Edinburgh.
His research focus has been on robot learning and decision-making under uncertainty, with emphasis on problems involving human-robot and multi-robot collaborative activities. These problems are solved using a combination of methods involving new representations based on geometric/topological abstractions, machine learning techniques with emphasis on issues of transfer, online and reinforcement learning, as well as game theoretic and behavioural models of inter-dependent decision making.
His work has been recognised by nominations for Best Paper Awards at major international conferences – ICRA 2008, IROS 2010, ICDL 2012 and EACL 2014. He serves in editorial and programme committee roles for conferences and journals in the areas of AI and Robotics. He leads Team Edinferno, the first UK entry in the Standard Platform League at the RoboCup International Competition. This work has received media coverage, including by BBC News and The Telegraph, and has resulted in many public engagement activities, such as at the Royal Society Summer Science Exhibition, Edinburgh International Science festival and Edinburgh Festival Fringe.
Before joining the School of Informatics, he was a Staff Engineer with National Instruments Corp., where he contributed to five products in the areas of motion control, computer vision and dynamic simulation. This work resulted in seven US patents and numerous industry awards for product innovation.