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CMU ML Lunch (April 28)

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Published on Apr 28, 2014

Title: Imitation Learning for Natural Language Direction Following through Unknown Environments
Speaker: Felix Duvallet

Abstract:
Commanding robots through unconstrained natural language directions is intuitive, flexible, and does not require specialized interfaces or training. Providing this capability would enable effortless coordination in human robot teams that operate in non-specialized environments. However, natural language direction following through unknown environments requires understanding the meaning of language, using a partial semantic world model to generate actions in the world, and reasoning about the environment and landmarks that have not yet been detected.

We address the problem of robots following natural language directions through complex unknown environments. By exploiting the structure of spatial language, we can frame direction following as a problem of sequential decision making under uncertainty. We learn a policy using imitation learning from demonstrations of people following directions. The trained policy predicts a sequence of actions that follow the directions, explores the environment (discovering new landmarks), backtracks when necessary, and explicitly declares when it has reached its destination. By training explicitly in unknown environments we can generalize to situations that have not been encountered previously.

This is work with Anthony Stentz (CMU), Tom Kollar (CMU), Matt Walter (MIT), Tom Howard (MIT), and Sachi Hemachandra (MIT).

For more ML Lunch talk, visit http://www.cs.cmu.edu/~learning/

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