Approaching the Symbol Grounding Problem with Probabilistic Graphical Models


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



n order for robots to engage in dialog with human teammates, they must have the ability to map between words in the language and aspects of the external world. A solution to this symbol grounding problem (Harnad, 1990) would enable a robot to interpret commands such as “Drive over to receiving and pick up the tire pallet.” In this article we describe several of our results that use probabilistic inference to address the symbol grounding problem. Our specific approach is to develop models that factor according to the linguistic structure of a command. We first describe an early result, a generative model that factors according to the sequential structure of language, and then discuss our new framework, generalized grounding graphs (G3). The G3 framework dynamically instantiates a probabilistic graphical model for a natural language input, enabling a mapping between words in language and concrete objects, places, paths and events in the external world. We report on corpus-based experiments where the robot is able to learn and use word meanings in three real-world tasks: indoor navigation, spatial language video retrieval, and mobile manipulation.




How to Cite

Tellex, S., Kollar, T., Dickerson, S., Walter, M. R., Banerjee, A. G., Teller, S., & Roy, N. (2011). Approaching the Symbol Grounding Problem with Probabilistic Graphical Models. AI Magazine, 32(4), 64-76.