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Aude Billard

Swiss physicist

Aude G. Billard (born c. August 6, 1971)[1] is a Swiss physicist in the fields of machine learning and human-robot interactions.[2] As a full professor at the School of Engineering at Swiss Federal Institute of Technology in Lausanne (EPFL), Billard’s research focuses on applying machine learning to support robot learning through human guidance. Billard’s work on human-robot interactions has been recognized numerous times by the Institute of Electrical and Electronics Engineers (IEEE) and she currently holds a leadership position on the executive committee of the IEEE Robotics and Automation Society (RAS) as the vice president of publication activities.[3]

Aude Gemma Billard

Portrait of Aude Billard

Born1971 (age 53–54)

Lausanne, Switzerland

NationalitySwiss
Alma materB.S. and M.S. École Polytechnique Fédérale de Lausanne (EPFL), M.S. and Ph.D. University of Edinburgh
Known forApplying machine learning to robotics to improve learning and task performance
Awards
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Aude Billard

Also published under: Aude G. Billard, A. Billard, A. G. Billard, Aude Gemma Billard

Affiliation

LASA, School of Engineering, EPFL (Swiss Federal Institute of Technology in Lausanne), Lausanne, Switzerland


Publication Topics

Obstacle Avoidance,System Dynamics,Joint Space,Learning Models,Path Planning,Robot Control,Robotic Hand,Dynamic Environment,Inverse Reinforcement Learning,Model Predictive Control,Navigation Function,Robot Manipulator,Tangential Direction,Task Space,Vector Field,Adaptive Control,Adaptive Law,Artificial Potential Field,Collision Detection,Dynamical,Gaussian Mixture Model,IEEE Transactions,Limit Cycle,Neural Network,Positive Definite Matrix,Quadratic Programming,Rapidly-exploring Random Tree,Real Robot,Robot Motion,Robotic Arm,Robotic System,Static Obstacles,Angular Velocity,Constrained Optimization Problem,Contact Point,Convex Optimization Problem,Cost Function,Definite Matrix,Distance Function,Dynamic Obstacles,Fixed Point,Free Space,Friction Coefficient,Goal Position,Hidden Markov Model,High-dimensional,Human Hand,Human Motion,Human Op
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Aude Billard

Dr. Aude Billard 

School of Engineering, École Polytechnique Fédérale de Lausanne

Talk Title:

Robots that think and act fast

Date:

May 7th, 2021

Time:

12-1:30 pm

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.

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