Imitation Learning as Cause-Effect Reasoning

نویسندگان

  • Garrett E. Katz
  • Di-Wei Huang
  • Rodolphe J. Gentili
  • James A. Reggia
چکیده

We propose a framework for general-purpose imitation learning centered on cause-effect reasoning. Our approach infers a hierarchical representation of a demonstrator’s intentions, which can explain why they acted as they did. This enables rapid generalization of the observed actions to new situations. We employ a novel causal inference algorithm with formal guarantees and connections to automated planning. Our approach is implemented and validated empirically using a physical robot, which successfully generalizes skills involving bimanual manipulation of composite objects in 3D. These results suggest that cause-effect reasoning is an effective unifying principle for cognitive-level imitation learning.

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تاریخ انتشار 2016