Abstract-We present an approach to robotic deep learning from demonstration in virtual reality, which combines a deep 3D convolutional neural network, for grasp detection from 3D point clouds, with domain randomization to generate a large training data set. The use of virtual reality (VR) enables robot learning from demonstration in a virtual environment. In this environment, a human user can easily and intuitively demonstrate examples of how to grasp an object, such as a fish. From a few dozen of these demonstrations, we use domain randomization to generate a large synthetic training data set consisting of 76 000 example grasps of fish. After training the network using this data set, the network is able to guide a gripper to grasp virtual fish with good success rates. Our domain randomization approach is a step towards an efficient way to perform robotic deep learning from demonstration in virtual reality.
Our previous work REF applied 3D convolutional neural networks (3D CNN) to robot grasping.
Grasping virtual fish: A step towards robotic deep learning from demonstration in virtual reality
2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)
2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Engineering Computer Science