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Standard computer vision systems assume access to intelligently capturedinputs (e.g., photos from a human photographer), yet autonomously capturinggood observations is a major challenge in itself. We address the problem oflearning to look around: how can an agent learn to acquire informative visualobservations? We propose a reinforcement learning solution, where the agent isrewarded for reducing its uncertainty about the unobserved portions of itsenvironment. Specifically, the agent is trained to select a short sequence ofglimpses after which it must infer the appearance of its full environment. Toaddress the challenge of sparse rewards, we further introduce sidekick policylearning, which exploits the asymmetry in observability between training andtest time. The proposed methods learn observation policies that not onlyperform the completion task for which they are trained, but also generalize toexhibit useful "look-around" behavior for a range of active perception tasks.

Emergence of Exploratory Look-Around Behaviors through Active Observation Completion