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Task-agnostic self-modeling machines

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Science Robotics  30 Jan 2019:
Vol. 4, Issue 26, eaau9354
DOI: 10.1126/scirobotics.aau9354
  • Fig. 1 Self-model generation, usage, and adaptation.

    An outline of the self-modeling process from data collection to task planning. (Step 1) The robot recorded action-sensation pairs. (Step 2) The robot used deep learning to create a self-model consistent with the data. (Step 3) The self-model could be used for internal planning of two separate tasks without any further physical experimentation. (Step 4) The robot morphology was abruptly changed to emulate damage. (Step 5) The robot adapted the self-model using new data. (Step 6) Task execution resumed.

    Credit: A. Kitterman/Science Robotics

Supplementary Materials

  • robotics.sciencemag.org/cgi/content/full/4/26/eaau9354/DC1

    Section S1. Related work

    Section S2. Experimental robot platform

    Section S3. Training the self-model

    Section S4. Results

    Section S5. Self-model adaptation to damage

    Section S6. Limitations

    Fig. S1. The robot and data used for this study.

    Fig. S2. Self-modeling process.

    Fig. S3. Self-model architecture and training.

    Fig. S4. Two tasks.

    Fig. S5. Accuracy degradation.

    Fig. S6. Diagram of the reach of the WidowX robot arm taken from Trossen Robotics.

    Fig. S7. Rendering of the CAD model used to 3D-print the deformed arm length.

    Fig. S8. Analytical model position versus physical robot position.

    Fig. S9. The distribution of accuracies when using the forward kinematics model.

    Fig. S10. The distribution of accuracies visualized onto the reachable space.

    Fig. S11. The forward kinematics learner architecture.

    Table S1. Summary of results of trajectory planning and pick-and-place tests.

    Table S2. Joint position.

    Table S3. Table of model parameters in the model used to conduct all self-modeling tests.

    Table S4. Table of positions the arm was instructed to go to in the pick-and-place test.

    References (916).

    Movie S1. Overview video.

    Data S1. 3D printed part model .stl file.

  • Supplementary Materials

    The PDF file includes:

    • Section S1. Related work
    • Section S2. Experimental robot platform
    • Section S3. Training the self-model
    • Section S4. Results
    • Section S5. Self-model adaptation to damage
    • Section S6. Limitations
    • Fig. S1. The robot and data used for this study.
    • Fig. S2. Self-modeling process.
    • Fig. S3. Self-model architecture and training.
    • Fig. S4. Two tasks.
    • Fig. S5. Accuracy degradation.
    • Fig. S6. Diagram of the reach of the WidowX robot arm taken from Trossen Robotics.
    • Fig. S7. Rendering of the CAD model used to 3D-print the deformed arm length.
    • Fig. S8. Analytical model position versus physical robot position.
    • Fig. S9. The distribution of accuracies when using the forward kinematics model.
    • Fig. S10. The distribution of accuracies visualized onto the reachable space.
    • Fig. S11. The forward kinematics learner architecture.
    • Table S1. Summary of results of trajectory planning and pick-and-place tests.
    • Table S2. Joint position.
    • Table S3. Table of model parameters in the model used to conduct all self-modeling tests.
    • Table S4. Table of positions the arm was instructed to go to in the pick-and-place test.
    • References (916).
    • Data S1. 3D printed part model .stl file.
    • Legend for Movie S1

    Download PDF

    Other Supplementary Material for this manuscript includes the following:

    • Movie S1 (.mp4 format). Overview video.

    Files in this Data Supplement:

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