DSAI seminar with Karinne Ramirez-Amaro

Abstract

The advances in Collaborative Robots (Cobots) have rapidly increased with the development of novel data- and knowledge-driven methods. These methods allow robots, to some extent, to explain their decisions. This research area is known as Explainable AI and is gaining importance in the robotics community. One advantage of such methods is the increase of human trust towards Cobots since robots could explain their decisions, especially when errors occur or when facing new situations. Explainability is a challenging and important component when deploying Cobots into real and dynamic environments. In this talk, I will introduce a novel semantic-based learning method that generates compact and general models to infer human activities. I will also explain our current learning approaches to enable Cobots to learn from experience. Reasoning and learning from experiences are key when developing general-purpose machine learning methods. These experiences will allow robots to remember the best strategies to achieve a goal. Therefore, the new generation of robots should reason based on past experiences while providing explanations in case of errors. Thus, improving the autonomy of robots and human’s trust to work with robots.

About the speaker

Dr. Karinne Ramirez-Amaro is an Associate professor at Chalmers University of Technology since March 2022. Previously, she was a post-doctoral researcher at the Technical University of Munich (TUM), Germany. She completed her Ph.D. (summa cum laude) at the Department of Electrical and Computer Engineering at TUM in 2015. She has received different awards, e.g. the price of excellent Doctoral degree for female engineering students and the Google Anita Borg scholarship. In 2022, Karinne was elected as member of the Administrative Committee (AdCom) from the IEEE Robotics and Automation Society (RAS) and she is the chair of the IEEE RAS Women in Engineering (WiE). Her research interests include Explainable AI, Semantic Representations, Cause-based Learning Methods, Collaborative Robotics, and Human Activity Recognition and Understanding.

This is a seminar from the DSAI seminars series usually held every Monday at 14:00 by the Data Science and AI division. The seminars are usually hybrid. No registration is required.