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We propose HyperDynamics, a dynamics meta-learning framework that conditionson an agent's interactions with the environment and optionally its visualobservations, and generates the parameters of neural dynamics models based oninferred properties of the dynamical system. Physical and visual properties ofthe environment that are not part of the low-dimensional state yet affect itstemporal dynamics are inferred from the interaction history and visualobservations, and are implicitly captured in the generated parameters. We testHyperDynamics on a set of object pushing and locomotion tasks. It outperformsexisting dynamics models in the literature that adapt to environment variationsby learning dynamics over high dimensional visual observations, capturing theinteractions of the agent in recurrent state representations, or usinggradient-based meta-optimization. We also show our method matches theperformance of an ensemble of separately trained experts, while also being ableto generalize well to unseen environment variations at test time. We attributeits good performance to the multiplicative interactions between the inferredsystem properties -- captured in the generated parameters -- and thelow-dimensional state representation of the dynamical system.

HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks