Inferring Maps and Behaviors from Natural Language Instructions
Author(s)
Duvallet, Felix
Oh, Jean
Stentz, Anthony
Walter, Matthew Robert
Howard, Thomas M.
Hemachandra, Sachithra Madhawa
Teller, Seth
Roy, Nicholas
Date Issued
November 2015
Journal
Experimental Robotics
Publisher
Springer Nature
Citation
Duvallet, Felix, Matthew R. Walter, Thomas Howard, Sachithra Hemachandra, Jean Oh, Seth Teller, Nicholas Roy, and Anthony Stentz. “Inferring Maps and Behaviors from Natural Language Instructions.” Experimental Robotics (November 2015): 373–388 © 2016 Springer International Publishing Switzerland
Version
Author's final manuscript
Abstract
Natural language provides a flexible, intuitive way for people to command robots, which is becoming increasingly important as robots transition to working alongside people in our homes and workplaces. To follow instructions in unknown environments, robots will be expected to reason about parts of the environments that were described in the instruction, but that the robot has no direct knowledge about. However, most existing approaches to natural language understanding require that the robot’s environment be known a priori. This paper proposes a probabilistic framework that enables robots to follow commands given in natural language, without any prior knowledge of the environment. The novelty lies in exploiting environment information implicit in the instruction, thereby treating language as a type of sensor that is used to formulate a prior distribution over the unknown parts of the environment. The algorithm then uses this learned distribution to infer a sequence of actions that are most consistent with the command, updating our belief as we gather Keywords
Natural Language; Mobile Robot; Parse Tree; World Model; Behavior Inference
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-319-23778-7_25
