Robust Constraint-consistent Learning

Many everyday human skills can be framed in terms of performing some task subject to constraints imposed l y the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we resent a novel approach for learning (unconstrained) control policies from movement data, where observations are recorded under different constraint settings. Our approach seamlessly integrates unconstrained and constrained observations by pert wining hybrid optimisation of two risk functionals. The first a novel risk functional that makes a meaningful comparison between the estimated policy and constrained observations. The second is the standard risk, used to reduce the expected error under impoverished sets of constraints. We demonstrate our approach on systems of varying complexity, and illustrate its utility for transfer learning of a car washing task from human motion capture data.