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A crucial capability of real-world intelligent agents is their ability toplan a sequence of actions to achieve their goals in the visual world. In thiswork, we address the problem of visual semantic planning: the task ofpredicting a sequence of actions from visual observations that transform adynamic environment from an initial state to a goal state. Doing so entailsknowledge about objects and their affordances, as well as actions and theirpreconditions and effects. We propose learning these through interacting with avisual and dynamic environment. Our proposed solution involves bootstrappingreinforcement learning with imitation learning. To ensure cross taskgeneralization, we develop a deep predictive model based on successorrepresentations. Our experimental results show near optimal results across awide range of tasks in the challenging THOR environment.

Visual Semantic Planning using Deep Successor Representations