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WIN & RBE Colloquium Series: Dr. Aude Billard | Robots that think and act fast


Various images of robots at Robotics Engineering WPI alt
WPI Robotics Engineering
Friday, May 07, 2021
12:00 pm to 1:30 pm

Robotics Engineering Colloquium Series

WIN ‘Bridging the Gaps’ Colloquium Series presents: 

Dr. Aude Billard 

School of Engineering, École Polytechnique Fédérale de Lausanne

Robots that think and act fast 


Friday, May 7, 2021 

12:00 PM – 1:30 PM 

Virtual: (sign-in required)  


Abstract: The next generation of robots will soon get out of the secure and predictable environment of factories and will face the complexity and unpredictability of our daily environments. To avoid that robots fail lamely at the task they are programmed to do, robots will need to adapt on the go. I will present techniques from machine learning to allow robots to learn strategies to enable them to react rapidly and efficiently to changes in the environment. Learning the set of feasible solutions will be preferred over learning optimal controllers. I will review methods we have developed to allow instantaneous reactions to perturbation, leveraging on the multiplicity of feasible solutions. I will present applications of these methods for compliant control during human-robot collaborative tasks and for performing fast motion, such as catching flying objects.

Bio: Aude Billard is Professor in the School of Engineering, École Polytechnique Fédérale de Lausanne. Her research interests span the control and design of robotic systems meant to interact with humans. To this goal, she pursue research in three complementary areas: a) the development of control systems for teaching robots through human demonstration; b) the study of the neural and cognitive processes underpinning imitation learning in humans; c) the design of user-friendly human-computer interfaces to facilitate human-robot interaction. She also conducts research on societal aspects of the use of robotics with application to diagnosis and therapy of children with autism. Her expertise lies in robot control, signal processing and machine learning, areas that are fundamental to her research and teaching.

Find out more about the Woman’s Impact Network (WIN) ‘Bridging the Gaps’ Colloquium Series:  

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