A case for robust AI in robotics

Researchers envision a world wherein robots are free to interact with the external environment, thereby including human beings, other living creatures, robots and a variety of inanimate objects. It is always tacitly assumed that interactions will be smooth, i.e., they will fulfill several desirable properties ranging from safety to appropriateness. We posit that a reasonable mathematical model to frame such vision is that of Markov decision processes, and that ensuring smooth interactions amounts to endow robots with control policies that are provably compliant with side conditions expressed in probabilistic temporal logic.