Our brains are able to exploit coarse physical models of fluids to quickly adapt and solve everyday manipulation tasks. However, developing such capability in robots, so that they can autonomously manipulate fluids adapting to different conditions remains a challenge. In this talk, I will present different strategies that a Robot can use to manipulate liquids by using approximate-but-fast simulation as an internal model. I'll describe strategies to pour and calibrate the parameters of the model from observations of real liquids with different viscosities via Bayesian Likelihood-free Inference. Finally, I'll present a methodology to learn the relevant parameters of a pouring task via Inverse Value Estimation and describe potential applications of the learned posterior to reason about containers and safety.
Bio: Tatiana Lopez-Guevara is a final year PhD student in Robotics and Autonomous Systems at the Edinburgh Centre for Robotics, UK. Her interests are in the application of intuitive physics models for robotic reasoning and manipulation of deformable objects.