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Keyframe-based Learning from Demonstration

Method and Evaluation

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Abstract

We present a framework for learning skills from novel types of demonstrations that have been shown to be desirable from a Human–Robot Interaction perspective. Our approach—Keyframe-based Learning from Demonstration (KLfD)—takes demonstrations that consist of keyframes; a sparse set of points in the state space that produces the intended skill when visited in sequence. The conventional type of trajectory demonstrations or a hybrid of the two are also handled by KLfD through a conversion to keyframes. Our method produces a skill model that consists of an ordered set of keyframe clusters, which we call Sequential Pose Distributions (SPD). The skill is reproduced by splining between clusters. We present results from two domains: mouse gestures in 2D and scooping, pouring and placing skills on a humanoid robot. KLfD has performance similar to existing LfD techniques when applied to conventional trajectory demonstrations. Additionally, we demonstrate that KLfD may be preferable when demonstration type is suited for the skill.

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Notes

  1. Using velocity and acceleration data along with position data helps to keep some of the dynamics of the demonstration.

  2. Not all LfD techniques have this problem, e.g. [17].

  3. C k continuity for a function means that the function’s 1…k derivatives exist and are all continuous.

  4. See e.g. http://www.youtube.com/watch?v=5lVxOKSeYsk.

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Acknowledgements

This research is supported by NSF CAREER grant IIS-1032254.

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Authors and Affiliations

  1. School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Baris Akgun, Maya Cakmak, Karl Jiang & Andrea L. Thomaz

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  1. Baris Akgun
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  2. Maya Cakmak
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  3. Karl Jiang
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  4. Andrea L. Thomaz
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Correspondence to Baris Akgun.

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Akgun, B., Cakmak, M., Jiang, K. et al. Keyframe-based Learning from Demonstration. Int J of Soc Robotics 4, 343–355 (2012). https://doi.org/10.1007/s12369-012-0160-0

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