Deep Learning Scooping Motion using Bilateral Teleoperations
This work addresses robot motion generation for specific tasks like scooping, but it appears incremental as it combines existing teleoperation and deep learning methods without broad breakthroughs.
The researchers developed a bilateral teleoperation system that uses deep learning to generate robot scooping motions by learning from human demonstrations, achieving adaptivity for similar scooping tasks.
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.