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Simultaneously learning actions and goals from demonstration


Our research aim is to develop interactions and algorithms for learning from naïve human teachers through demonstration. We introduce a novel approach to leverage the goal-oriented nature of human teachers by learning an action model and a goal model simultaneously from the same set of demonstrations. We use robot motion data to learn an action model for executing the skill. We use a generic set of perceptual features to learn a goal model and use it to monitor the executed action model. We evaluate our approach with data from 8 naïve teachers demonstrating two skills to the robot. We show that the goal models in the perceptual feature space are consistent across users and correctly recognize demonstrations in cross-validation tests. We additionally observe that a subset of users were not able to teach a successful action model whereas all of them were able to teach a mostly successful goal model. When the learned action models are executed on the robot, the success was on average 66.25 %. Whereas the goal models were on average 90 % correct at deciding on success/failure of the executed action, which we call monitoring.

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  1. 1.

    We only assume that a segmentation for objects is available.

  2. 2.

  3. 3.

    Integrating more robust object segmentation into the perception pipeline is left for future work. Since our experiments in this article are performed on a batch of demonstrations offline, robust online tracking is not our current focus. This will become important in our future work when we want learning to be an incremental online process, and we believe solutions exist for obtaining a robust segmentation for this purpose.

  4. 4.

    This object based representation is fairly common in robotics.

  5. 5.

    These details are given for completeness but another model selection procedure can be used as well.

  6. 6.

    Although tractable approximate methods exist for DBNs.

  7. 7.

    They are mirrored since the participant is standing across the table in one and standing next to the robot in the other.

  8. 8.

    Participant 1 has only provided between 2 or 3 keyframes per demonstration whereas other participants provided 4–6. As a result, participant 1s goal model was not able to recognize the demonstrations of other users.

  9. 9.

    The cross-validation tests how similar the demonstrations are but not how the action itself is modelled.

  10. 10.

    In an interactive scenario, the teacher might realize this and fix it with their follow-up demonstrations.

  11. 11.

    Fast enough to have a fluid interaction with the user.

  12. 12.

    Expert in the sense of demonstrations, not necessarily the underlying algorithms.

  13. 13.

    We can represent cyclic behaviors with the current action model but currently have no means to decide on when to stop the cycle, see Sect. 4.6.


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This work has been supported by US National Science Foundation CAREER award #0953181, and by US Office of Naval Research grant #N000141410120.

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Correspondence to Baris Akgun.

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Akgun, B., Thomaz, A. Simultaneously learning actions and goals from demonstration. Auton Robot 40, 211–227 (2016).

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  • Learning from demonstration
  • Goal learning
  • Human–robot interaction