  Learning from expert demonstrations to flexibly program an autonomous system
with complex behaviors or to predict an agent's behavior is a powerful tool,
especially in collaborative control settings. A common method to solve this
problem is inverse reinforcement learning (IRL), where the observed agent,
e.g., a human demonstrator, is assumed to behave according to the optimization
of an intrinsic cost function that reflects its intent and informs its control
actions. While the framework is expressive, it is also computationally
demanding and generally lacks convergence guarantees. We therefore propose a
novel, stability-certified IRL approach by reformulating the cost function
inference problem to learning control Lyapunov functions (CLF) from
demonstrations data. By additionally exploiting closed-form expressions for
associated control policies, we are able to efficiently search the space of
CLFs by observing the attractor landscape of the induced dynamics. For the
construction of the inverse optimal CLFs, we use a Sum of Squares and formulate
a convex optimization problem. We present a theoretical analysis of the
optimality properties provided by the CLF and evaluate our approach using both
simulated and real-world data.
