An Intelligence Architecture for Grounded Language Communication with Field Robots
Thomas M. Howard, Ethan Stump, Jonathan Fink, Jacob Arkin, Rohan Paul, Daehyung Park, Subhro Roy, Daniel Barber, Rhyse Bendell, Karl Schmeckpeper, Junjiao Tian, Jean Oh, Maggie Wigness, Long Quang, Brandon Rothrock, Jeremy Nash, Matthew R. Walter, Florian Jentsch, Nicholas Roy
Abstract
For humans and robots to collaborate effectively as teammates in unstructured environments, robots must be able to construct semantically rich models of the environment, communicate efficiently with teammates, and perform sequences of tasks robustly with minimal human intervention, as direct human guidance may be infrequent and/or intermittent. Contemporary architectures for human-robot interaction often rely on engineered human-interface devices or structured languages that require extensive prior training and inherently limit the kinds of information that humans and robots can communicate. Natural language, particularly when situated with a visual representation of the robot's environment, allows humans and robots to exchange information about abstract goals, specific actions, and/or properties of the environment quickly and effectively. In addition, it serves as a mechanism to resolve inconsistencies in the mental models of the environment across the human-robot team. This article details a novel intelligence architecture that exploits a centralized representation of the environment to perform complex tasks in unstructured environments. The centralized environment model is informed by a visual perception pipeline, declarative knowledge, deliberate interactive estimation, and a multimodal interface. The language pipeline also exploits proactive symbol grounding to resolve uncertainty in ambiguous statements through inverse semantics. A series of experiments on three different, unmanned ground vehicles demonstrates the utility of this architecture through its robust ability to perform language-guided