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Robots deployed to the real world must be able to interact with other agentsin their environment. Dynamic game theory provides a powerful mathematicalframework for modeling scenarios in which agents have individual objectives andinteractions evolve over time. However, a key limitation of such techniques isthat they require a-priori knowledge of all players' objectives. In this work,we address this issue by proposing a novel method for learning players'objectives in continuous dynamic games from noise-corrupted, partial stateobservations. Our approach learns objectives by coupling the estimation ofunknown cost parameters of each player with inference of unobserved states andinputs through Nash equilibrium constraints. By coupling past state estimateswith future state predictions, our approach is amenable to simultaneous onlinelearning and prediction in receding horizon fashion. We demonstrate our methodin several simulated traffic scenarios in which we recover players' preferencesfor, e.g., desired travel speed and collision-avoidance behavior. Results showthat our method reliably estimates game-theoretic models from noise-corrupteddata that closely matches ground-truth objectives, consistently outperformingstate-of-the-art approaches.

Online and Offline Learning of Player Objectives from Partial Observations in Dynamic Games