Understanding natural language commands for robotic navigation and mobile manipulation
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Teller_Understanding natural.pdf
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389.17 KB
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Author(s)
Tellex, Stefanie A.
Kollar, Thomas Fleming
Dickerson, Steven R.
Walter, Matthew R.
Banerjee, Ashis
Teller, Seth
Roy, Nicholas
Date Issued
August 2011
Journal
Twenty-Fifth AAAI Conference on Artificial Intelligence
Publisher
AAAI Publications
Citation
Tellex, S. et al. "Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation." AAAI Conference on Artificial Intelligence, North America, aug. 2011.
Version
Author's final manuscript
Abstract
This paper describes a new model for understanding natural language commands given to autonomous systems that perform navigation and mobile manipulation in semi-structured environments. Previous approaches have used models with fixed structure to infer the likelihood of a sequence of actions given the environment and the command. In contrast, our framework, called Generalized Grounding Graphs, dynamically instantiates a probabilistic graphical model for a particular natural language command according to the command's hierarchical and compositional semantic structure. Our system performs inference in the model to successfully find and execute plans corresponding to natural language commands such as "Put the tire pallet on the truck." The model is trained using a corpus of commands collected using crowdsourcing. We pair each command with robot actions and use the corpus to learn the parameters of the model. We evaluate the robot's performance by inferring plans from natural language commands, executing each plan in a realistic robot simulator, and asking users to evaluate the system's performance. We demonstrate that our system can successfully follow many natural language commands from the corpus.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3623/4113
