Machine learning for robots to think fast

Dexterous manipulation of objects is robotics’ 21st century primary goal. It envisions robots capable of sorting objects and packaging them, of chopping vegetables and folding clothes, and this, at high speed. To manipulate these objects cannot be done with traditional control approaches, for lack of accurate models of objects and contact dynamics. Robotics leverages, hence, the immense progress in machine learning to encapsulate models of uncertainty and to support further advances on adaptive and robust control.

I will present applications of machine learning for controlling robots to: a) learn non-linear control laws in closed-form, which enables fast retrieval and adaptation at run time – and have robots catch flying objects; b) model complex deformations of objects – to peel and grate vegetables; c) learn manifolds, as embedding of feasible solutions and extract latent spaces in which stability of control laws can be more easily ensured.