Final Report Summary - CON-HUMO (Control based on Human Models)
First, we systematically analyzed specifications of control-relevant characteristics such as equilibria, stability, controllability and observability in data-driven models. By mathematically deriving the stability properties, equilibrium distributions and learning behavior of data-driven models, we were able to enforce stochastic stability conditions for control applications.
Second, we developed stable robot control designs based on data-driven dynamics models. In particular, we investigated a class of stochastic optimal control approaches for robotics applications that explicitly incorporates the prediction uncertainty of GPs and other probabilistic representations of the model into the control design Furthermore, we developed a constrained control approach, which is crucial for guaranteeing safety in physical HRI. With these results, we demonstrated that this type of controls significantly improves the human-perceived quality of robotic assistance while ensuring the interaction is guaranteed to be safe for the humans. Third, to generate human model models for control designs, we developed methods for modelling dynamics of the human behavior. To this end, we employed data-driven identification of dynamical systems as they are advantageous when no analytical model is available and it requires only minimal prior knowledge. As a result, it enabled a more accurate prediction of human motion behavior with psychological theories of human-human interaction, which is an important precursor to generate intuitive robot behaviors. Altogether, the advancements of control theories and modelling techniques for stochastic non-linear systems in the CON-HUMO project demonstrated examples of how intuitive, effective and safe (physical) HRI can be realized in mathematical frameworks with formal guarantees for control stability.