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Abstract:
The use of robots in our everyday life is hindered by the complexity necessary to design and tune appropriate controllers to execute the desired tasks.
In this talk, I will show how Bayesian modelling can help to substantially reduce such complexity by providing effective tools.
In the first part of my talk, I will discuss the learning of dynamical models required for accurate control and planning of the robot's movement, with a special emphasis on discontinuities deriving from contacts with the environment.
Following, I will discuss the use of Bayesian optimization to efficiently optimize the parameters of existing controllers. As demonstration, I will present results obtained on a dynamic bipedal walker.
Short Bio:
Roberto Calandra is a PhD Candidate in the Autonomous Intelligent Systems Lab at TU Darmstadt, Germany. Previously, he achieved a B.Sc. in Computer Science with an emphasis on control at the University of Palermo, Italy and a M.Sc. in Machine Learning and Data Mining at the Aalto University (formerly Helsinki University of Technology), Finland.
His research interest lie at the convergence between robotics and machine learning.
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