Contextual awareness: Understanding monologic natural language instructions for autonomous robots

Today, there are many examples of humans and robots regularly interacting in a variety of domains, such as manufacturing, coordinated assembly, and rehabilitation. A resulting demand for more generally accessible communication interfaces has motivated several recent independent research efforts focused on providing robotic systems with a robust natural language interface. Natural language interfaces enable intuitive interaction for untrained and non-expert users. However, achieving real-time performance is particularly challenging, yet essential, to enable flexible, efficient communication. The length of the language input directly impacts the run-time performance and quickly becomes a practical issue when the input is a sequence of multiple sentences, or a monologue. In this work, we propose a variant of a contemporary probabilistic graphical model for language understanding that introduces novel segmentation of the input into a sequence of sentences to be labeled in order. We introduce the notion of a continuously updated prior context that retains the meaning of previous sentences as the inference process proceeds. This prior context serves as evidence during future sentence evaluations. We evaluate our model on two natural language corpora, and demonstrate its utility on a Clearpath Husky A200 mobile manipulator and a simulated Rethink Robotics Baxter Robot.