  We present an approach to continuous perception for robotic laundry tasks.
Our assumption is that the visual prediction of a garment's shapes and weights
is possible via a neural network that learns the dynamic changes of garments
from video sequences. Continuous perception is leveraged during training by
inputting consecutive frames, of which the network learns how a garment
deforms. To evaluate our hypothesis, we captured a dataset of 40K RGB and 40K
depth video sequences while a garment is being manipulated. We also conducted
ablation studies to understand whether the neural network learns the physical
and dynamic properties of garments. Our findings suggest that a modified
AlexNet-LSTM architecture has the best classification performance for the
garment's shape and weights. To further provide evidence that continuous
perception facilitates the prediction of the garment's shapes and weights, we
evaluated our network on unseen video sequences and computed the 'Moving
Average' over a sequence of predictions. We found that our network has a
classification accuracy of 48% and 60% for shapes and weights of garments,
respectively.
