Unsupervised Learning of Sensorimotor Affordances by Stochastic Future Prediction.

Recently, much progress has been made building systems that can capture static image properties, but natural environments are intrinsically dynamic. For an intelligent agent, perception is responsible not only for capturing features of scene content, but also capturing its textit{affordances}: how the state of things can change, especially as the result of the agentu0027s actions. We propose an unsupervised method to learn representations of the sensorimotor affordances of an environment. We do so by learning an embedding for stochastic future prediction that is (i) sensitive to scene dynamics and minimally sensitive to static scene content and (ii) compositional in nature, capturing the fact that changes in the environment can be composed to produce a cumulative change. We show that these two properties are sufficient to induce representations that are reusable across visually distinct scenes that share degrees of freedom. We show the applicability of our method to synthetic settings and its potential for understanding more complex, realistic visual settings.