Learning context-based outcomes for mobile robots in unstructured indoor environments

Abstract

We present a method to learn context-dependent outcomes of behaviors in unstructured indoor environments. The idea is that certain features in the environment may be predictive of differences in outcomes, such as how long a mobile robot takes to traverse a corridor. Doing so enables the robot to plan more effectively, and also be able to interact with people more effectively by more accurately predicting when its plans may take longer to execute or may be likely to fail. We use a node-and-edge based map of the environment and treat the traversal time of the robot for each edge as a random variable to be characterized. The first step is to determine whether the distribution of the random variable is multimodal and, if so, we learn to classify the modes using a hierarchy of plan-time features (e.g., time of the day, day of the week) and run-time features (observations of recent traversal times through other corridors). We utilize a cascading regression system that first estimates which mode of the traversal distribution we expect the robot to observe, and then predict the actual traversal time through a corridor. On average, our method produces a mean residual error of less than 2.7 seconds.