5 accepted papers
The choice of an appropriate representation is important when reconstructing motion from images. In this letter we propose a new type of deep neural network (DNN) that maps images to spatial paths represented by a recently introduced motion representation called arc-length dynamic movement primitive
In this paper we propose a new approach for learning perception-action couplings. We show that by collecting a suitable set of raw images and the associated movement trajectories, a deep encoder-decoder network can be trained that takes raw images as input and outputs the corresponding dynamic movem…
Encoding desired motions into dynamic movement primitives (DMPs) is a common way for generating compact task representations that are able to handle sensor-based goal adaptations. At the same time, a robot should not only express adaptive motion capabilities at planning level, but use also contact w…
Continuous and on-line adaptation of robotic trajectories is one of the key properties of robotic control policies that make robots useful in unstructured environments, where the conditions of the external world are not predefined or stationary. Modification of robotic trajectories through human int
Human-demonstrated motion transferred to a robotic platform often needs to be adapted to the current state of the environment or to modified task requirements. Adaptation, i. e. learning of a modified behavior, needs to be fast to enable quick utilization of the robot either in industry or in future…