Animals and humans are excellent in conceiving of solutions to physical and geometric problems, for instance in using tools, coming up with creative constructions, or eventually inventing novel mechanisms and machines. Cognitive scientists coined the term intuitive physics in this context. It is a shame we do not yet have good computational models of such capabilities.
A main stream of current robotics research focusses on training robots for narrow manipulation skills - often using massive data from physical simulators. Complementary to that we should also try to understand how basic principles underlying physics can directly be used to enable general purpose physical reasoning in robots, rather than sampling data from physical simulations. In this talk I will discuss an approach called Logic-Geometric Programming, which builds a bridge between control theory, AI planning and robot manipulation. It demonstrates strong performance on sequential manipulation problems, but also raises a number of highly interesting fundamental problems, including its probabilistic formulation, reactive execution and learning.
Biography: Marc Toussaint is professor for Machine Learning and Robotics at the University of Stuttgart since 2012 and Max Planck Fellow at the MPI for Intelligent Systems since November 2018. In 2017/18 he spend a year as visiting scholar at MIT, and before that some months with Amazon Robotics. His research aims to bridge between machine learning, control theory and AI planning, motivated by fundamental questions in robotics. Reoccurring themes in his research are appropriate representations and priors to enable efficient learning and reasoning in the real world, combining geometry, logic and probabilities in learning and reasoning, and active learning and exploration. His work was awarded best paper at R:SS'18, ICMLA'07 and runner up at R:SS'12, UAI'08.