Research ArticleSOFT ROBOTS

Soft optoelectronic sensory foams with proprioception

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Science Robotics  28 Nov 2018:
Vol. 3, Issue 24, eaau2489
DOI: 10.1126/scirobotics.aau2489
  • Fig. 1 Foam assembly design.

    (A) Left: Foam and optical fiber assembly in three stages of fabrication. Right: Cross section of foam and optical fiber assembly in three stages of fabrication. (B) Diagram of foam and optical fiber assembly.

  • Fig. 2 Sensor functionality.

    (A and B) Optical fiber terminals from which light intensity is read. (C to E) Real images of deformed foam and optical fiber assembly. (F to H) Real images of deformed foam and optical fiber assembly overlaid with computer reconstruction of the assembly’s state.

  • Fig. 3 Results from k-fold cross-validation.

    Error bars represent SD across the k models.

  • Fig. 4 Effect of training data size.

    Classification and regression performance on test data as a function of training data size. Each plot point represents the mean across random trials, and the error bars represent SD across 300 trials (15 for MLPs). Classification error is 0-1 loss.

  • Fig. 5 Effect of feature set size.

    Classification and regression performance on test data as a function of feature set size. Each plot point represents the mean across random trials, and the error bars represent SD across 300 trials (11 for MLPs).

  • Fig. 6 Experimental setup.

    (A) Bird’s eye view of experimental setup. (B) Diagram illustrating how each fiber serves as an illuminator and light detector via a beam splitter.

  • Fig. 7 Gathering data.

    (A and B) Real images of foam and optical fiber assembly during deformation in darkness. (C) Schematic of training data collection process.

  • Table 1 Classifier model error rate.

    Error rates of the classification models.

    kNNSVMsTree
    000.05
  • Table 2 Single-output regression model errors.

    Mean absolute errors for each deformation mode. CW, clockwise; CCW, counterclockwise.

    kNNGPsMLPsSVMsTreeLinear
    Bend up0.071.591.743.105.026.50
    Bend down0.081.882.722.925.258.92
    Twist CW01.812.361.984.505.87
    Twist CCW0.072.052.393.274.435.92
    Mean0.061.832.302.824.806.80
  • Table 3 Multi-output regression model errors.

    Mean absolute errors.

    kNNMLPsLinear
    0.011.4313.9

Supplementary Materials

  • Supplementary Materials

    The PDF file includes:

    • Fig. S1. Random versus greedy feature removal.
    • Table S1. Model parameters for best prediction models.
    • Table S2. Model evaluation times.

    Download PDF

    Other Supplementary Material for this manuscript includes the following:

    • Movie S1 (.mp4 format). Real-time deformation prediction.

    Files in this Data Supplement:

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