HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks

We propose HyperDynamics, a dynamics meta-learning framework that conditions on an agent's interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system. Physical and visual properties of the environment that are not part of the low-dimensional state yet affect its temporal dynamics are inferred from the interaction history and visual observations, and are implicitly captured in the generated parameters. We test HyperDynamics on a set of object pushing and locomotion tasks. It outperforms existing dynamics models in the literature that adapt to environment variations by learning dynamics over high dimensional visual observations, capturing the interactions of the agent in recurrent state representations, or using gradient-based meta-optimization. We also show our method matches the performance of an ensemble of separately trained experts, while also being able to generalize well to unseen environment variations at test time. We attribute its good performance to the multiplicative interactions between the inferred system properties--captured in the generated parameters-- and the low-dimensional state representation of the dynamical system. Humans learn dynamics models that predict results of their interactions with the environment, and use such predictions for selecting actions to achieve intended goals (Miall & Wolpert, 1996; Haruno et al., 1999). These models capture intuitive physics and mechanics of the world and are remarkably versatile: they are expressive and can be applied to all kinds of environments that we encounter in our daily lives, with varying dynamics and diverse visual and physical properties. In addition, humans do not consider these models fixed over the course of interaction; we observe how the environment behaves in response to our actions and quickly adapt our model for the situation at hand based on new observations. Let us consider the scenario of moving an object on the ground. We can infer how heavy the object is by simply looking at it, and we can then decide how hard to push.