Towards Safe Interactions with Intelligent Robots

Dr. Subramanian Ramamoorthy

Associate Professor School of Informatics University of Edinburgh

The confluence of advances in diverse areas including machine learning, large scale computing and reliable commoditised hardware have brought autonomous robots to the point where they are poised to be genuinely a part of our daily lives. Application areas where these autonomous robots must interact with human co-workers also bring with them stringent requirements regarding safety, explainability and trustworthiness. These needs seem to be at odds with the ways in which recent AI successes have been achieved, e.g., with end-to-end learning.

In this talk, I will describe key elements of these recent advances, using as examples robot systems developed in our lab. We will look at systems that are capable of intention-aware navigation in human environments, collaborative work with human co-workers and the use of dialogue to faciliate task specification by the layperson. A common theme underpinning these systems is the use of machine learning techniques, ranging from Bayesian inference to deep generative models.

I will end by describing how researchers are beginning to address the gaps between powerful learning methods and understandably safe systems, through the use of structured representations.

About Dr. Subramanian Ramamoorthy

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 an Executive Committee Member for the Edinburgh Centre for Robotics and at the Bayes Centre. 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, and has been a Visiting Professor at Stanford University and the University of Rome "La Sapienza". He also serves as Vice President - Prediction and Planning at FiveAI, a UK-based startup company in the autonomous vehicles space. His research focus is on robot learning and decision-making under uncertainty, addressed through a combination machine learning techniques with emphasis on issues of transfer, online and reinforcement learning as well as new representations and analysis techniques based on geometric/ topological abstractions.

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