Exploration and Prediction : Beyond-the-Frontier Autonomous Exploration in Indoor Environments

Autonomous exploration is a fundamental problem in robotics, where a robot must make decisions about how to navigate and map an unknown environment. While humans rely on prior experience and structural expectations to act under uncertainty, robotic systems typically operate without such priors, exploring reactively based only on what has been observed. The idea of incorporating predictions into exploration has been proposed previously, but the tools required to learn general, high-capacity models have only recently become available through advances in deep learning. This thesis addresses two tightly connected challenges: learning predictive models of indoor environments, and constructing exploration strategies that are able to benefit from such predictions. A core obstacle in this research area is a cyclic dependency: there is little value in developing better predictive models unless exploration methods can make effective use of them, and little value in de- signing such exploration methods unless reliable models exist. This dependency has historically limited progress. By breaking it, this thesis enables the study and development of both components in tandem. The thesis introduces deep generative models that capture structural regularities in indoor environments using autoregressive sequence modeling. These models outperform traditional approaches in predicting unseen regions beyond the robot’s current observations. However, standard exploration methods are shown to perform worse, not better, when informed by accurate predictions. To resolve this, new planning heuristics are proposed, including the distance advantage strategy, which prioritizes exploring regions that are likely to be more difficult to reach in the future. These methods allow predictive models to be used effectively, reducing path length by avoiding situations where the robot must backtrack to previously visited locations. Together, these contributions provide a foundation for autonomous exploration that is informed by learned expectations, and establish a framework where map-predictive modeling and decision-making can be studied and improved jointly.