Hierarchical Spatio-Temporal Morphable Models for Representation of complex movements for Imitation Learning

Imitation learning is a promising technique for teaching robots complex movement sequences. One key problem in this area is the transfer of perceived movement characteristics from perception to action. For the solution of this problem, representations are required that are suitable for the analysis and the synthesis of complex action sequences. We describe the method of Hierarchical Spatio-Temporal Morphable Models that allows an automatic segmentation of movements sequences into movement primitives, and a modeling of these primitives by morphing between a set of prototypical trajectories. We use HSTMMs in an imitation learning task for human writing movements. The models are learned from recorded trajectories and transferred to a human-like robot arm. Due to the generalization proper- ties of our movement representation, the arm is capable of synthesizing new writing movements with only a few learning examples

Exploiting temporal stability and low-rank structure for motion capture data refinement

Inspired by the development of the matrix completion theories and algorithms, a low-rank based motion capture (mocap) data refinement method has been developed, which has achieved encouraging results. However, it does not guarantee a stable outcome if we only consider the low-rank property of the motion data. To solve this problem, we propose to exploit the temporal stability of human motion and convert the mocap data refinement problem into a robust matrix completion problem, where both the low-rank structure and temporal stability properties of the mocap data as well as the noise effect are considered. An efficient optimization method derived from the augmented Lagrange multiplier algorithm is presented to solve the proposed model. Besides, a trust data detection method is also introduced to improve the degree of automation for processing the entire set of the data and boost the performance. Extensive experiments and comparisons with other methods demonstrate the effectiveness of our approaches on both predicting missing data and de-noising. © 2014 Elsevier Inc. All rights reserved

Planning of Joint Trajectories for Humanoid Robots Using B-Spline Wavelets

The formulation and optimization of joint trajectories for humanoid robots is quite different from this same task for standard robots because of the complexity of the humanoid robots' kinematics. In this paper we exploit the similarity between the movements of a humanoid robot and human movements to generate joint trajectories for such robots. In particular, we show how to transform human motion information captured by an optical tracking device into a high dimensional trajectory of a humanoid robot. We utilize B-spline wavelets to efficiently represent the joint trajectories and to automatically select the density of the basis functions on the time axis. We applied our method to the task of teaching a humanoid robot how to make a dance movement. 1 Introduction Movements of most of the current robot manipulators can be described by a single open kinematic chain. A standard approach to the specification of movement tasks for such robots is to define a trajectory for the motion of a ro..

Neues Konzept zur Planung, Ausführung und Überwachung von Roboteraufgaben mit hierarchischen Petri-Netzen

Es wird gezeigt, wie die aufgabenausführungsrelevanten Komponenten einer hybriden Steuerungsarchitektur mit Hilfe von hierarchischen Petri-Netzen umgesetzt, integriert und mit Überwachungsmodulen verknüpft werden können. Hierzu wird zunächst ein Konzept zur Generierung von Aufgabenwissen vorgeschlagen, das es erlaubt Bausteine komplexer Handlungen systematisiert zu entwerfen. Im Anschluss wird ein neues Konzept zur online Überwachung von Bewegungsvorgängen bei humanoiden Robotern vorgestellt