IEEE Robotics and Automation Letters (RA-L) 2020: Learning Transformable and Plannable se(3) Features for Scene Imitation of a Mobile Service Robot
Deep neural networks facilitate visuosensory inputs for robotic systems. However, the features encoded in a network without specific constraints have little physical meaning. In this research, we add constraints on the network so that the trained features are forced to represent the actual twist coordinates of interactive objects in a scene. The trained coordinates describe 6d-pose of the objects, and SE(3) transformation is applied to change the coordinate system. This algorithm is developed...