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About this paper
Thomas Kollar, Stefanie Tellex, Matthew Walter, Albert Huang, Abraham Bachrach, Sachi Hemachandra, Emma Brunskill, Ashis Banerjee and 3 more
The Generalized Grounding Graphs (G^3) framework enables robots to interpret natural language commands by dynamically constructing a probabilistic graphical model based on linguistic parse trees. It learns word meanings as probabilistic predicates and demonstrates robust command following capabilities across mobile manipulation and navigation tasks, outperforming baseline methods.
Key insights
- Dynamically structuring a probabilistic graphical model based on a command's linguistic parse tree effectively captures compositional and hierarchical language for robust command interpretation.
- Learning word meanings as probabilistic predicates allows the system to generalize to novel commands by compositionally combining learned concepts, reducing reliance on manual symbolic definitions.
- For sequential commands like route directions, a flattened linguistic model combined with efficient exploring local inference achieves performance comparable to global inference without requiring a complete map, demonstrating computational tractability.
Summary by alphaXiv.
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