Thinking in front of the box: Towards intelligent robotic action selection for navigation in complex environments using image-schematic reasoning

One of the problems an agent faces when operating in a partially known, dynamic, sometimes unpredictable environment is to keep track of aspects of the world relevant to its task, and, if possible, restrict its attention to only these aspects. We present our first steps towards constructing a system that combines
image schematic knowledge and reasoning with reactive robotics, and which enables perception that focuses on, and keeps track of, relevant entities and relationships. While our approach is more reasoning intensive than is usual in reactive robotics, the formalism we use for inference is fast and allows an agent to adjust, in real time, the complexity of its action selection procedures according to the complexity of the relevant part of the environment. We illustrate our approach with a few simulated examples of robots performing navigation tasks. In some examples, interaction with obstacles is necessary to complete the navigation tasks, adding complexity to the scenario.