Light Weight Dilated CNN for Time Series Classification and Prediction

Optimal decision making in robotic assembly and other trial-and-error tasks

“…This is because each iteration of the sequence can achieve partial insertion progress, and only at the end of each iteration is the seated status of the bearing is checked by the condition "At Z." As classifier, we are using a dilated convolutional neural network following the approach for time-series classification in [13], which has been pre-trained on 250 task attempts.…”

“…As individual snap shots of F/T data are inconclusive, we chose a dilated Fully Convolutional Network (FCN) to classify sequences of F/T data from the insertion behaviors described above (Figure 5B-D). Specifically, we chose a dilated FCN of the same architecture as Khanna and Narayan [13]. Our model differs from [13] in the following ways; Dilated Convolution Layer 1 (No.…”

“…Specifically, we chose a dilated FCN of the same architecture as Khanna and Narayan [13]. Our model differs from [13] in the following ways; Dilated Convolution Layer 1 (No. of filters = 16 rather than 8, Filter Size = 4 rather than 2, Dilation Rate = 4 rather than 2), Dilated Convolution Layer 2 (No.…”

“…of filters = 16 rather than 8, Filter Size = 4 rather than 2, Dilation Rate = 4 rather than 2), Dilated Convolution Layer 2 (No. of filters = 16 rather than 8, Filter Size = 4 rather than 2, Dilation Rate = 8 rather than 4), and a Fully Connected Layer of 50 units (number of units in this layer not given by [13]). Although Recurrent Neural Networks (RNN), such as GRU and LSTM, have shown admirable performance in failure prediction, [40,41,42], simpler FCN have shown similar or better performance [43,44,45].…”