  Inferring the intent of an intelligent agent from demonstrations and
subsequently predicting its behavior, is a critical task in many collaborative
settings. A common approach to solve this problem is the framework of inverse
reinforcement learning (IRL), where the observed agent, e.g., a human
demonstrator, is assumed to behave according to an intrinsic cost function that
reflects its intent and informs its control actions. In this work, we
reformulate the IRL inference problem to learning control Lyapunov functions
(CLF) from demonstrations by exploiting the inverse optimality property, which
states that every CLF is also a meaningful value function. Moreover, the
derived CLF formulation directly guarantees stability of inferred control
policies. We show the flexibility of our proposed method by learning from
goal-directed movement demonstrations in a continuous environment.
