Abstract

The concept of affordance is commonly described as the set of possibilities an environment offers an animal to select and execute actions. In the context of autonomous robot navigation, determining which affordances are available to a mobile agent provides crucial information to perform navigation tasks such as executing navigation plans or exploring new environments. In this work, we provide a robot with the capability of detecting changes in the set of navigation affordances that it has available as it moves through indoor spaces. Specifically, given a navigation plan represented as a sequence of high-level behaviors such as 'turn left' or 'follow a corridor', we propose the Affordance-Based Plan Executor (APEX) as a new learning-based method to execute this plan based on the identification of navigation affordances. As a relevant fact, the proposed method does not require a map of the environment at execution time. To the best of our knowledge, our work is one of the first to explicitly consider the identification of navigation affordances as a central element to implement the execution of a behavior-based navigation plan in an indoor environment. Our experiments using the Gibson simulator and environments from the Stanford 2D-3D-S dataset suggest that our approach performs well in previously seen environments in comparison to baselines, exceeding alternative approaches' success rate by up to 43%. Additionally, our approach shows promise at generalizing to previously unseen environments, exceeding the success rate of alternative approaches by up to 26.3%. Finally, our experiments confirm that explicit reasoning about affordances is key to APEX's performance.