Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation


  • Stefanie Tellex Massachusetts Institute of Technology
  • Thomas Kollar Massachusetts Institute of Technology
  • Steven Dickerson Massachusetts Institute of Technology
  • Matthew Walter Massachusetts Institute of Technology
  • Ashis Banerjee Massachusetts Institute of Technology
  • Seth Teller Massachusetts Institute of Technology
  • Nicholas Roy Massachusetts Institute of Technology


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.




How to Cite

Tellex, S., Kollar, T., Dickerson, S., Walter, M., Banerjee, A., Teller, S., & Roy, N. (2011). Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 1507-1514. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7979