  We present bilateral teleoperation system for task learning and robot motion
generation. Our system includes a bilateral teleoperation platform and a deep
learning software. The deep learning software refers to human demonstration
using the bilateral teleoperation platform to collect visual images and robotic
encoder values. It leverages the datasets of images and robotic encoder
information to learn about the inter-modal correspondence between visual images
and robot motion. In detail, the deep learning software uses a combination of
Deep Convolutional Auto-Encoders (DCAE) over image regions, and Recurrent
Neural Network with Long Short-Term Memory units (LSTM-RNN) over robot motor
angles, to learn motion taught be human teleoperation. The learnt models are
used to predict new motion trajectories for similar tasks. Experimental results
show that our system has the adaptivity to generate motion for similar scooping
tasks. Detailed analysis is performed based on failure cases of the
experimental results. Some insights about the cans and cannots of the system
are summarized.
