A Novel Multimodal Emotion Recognition Approach for A ective Human Robot Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Felipe Cid, Luis J. Manso and Pedro Nun~ez A Multi-modal Approach for Assistive Humanoid Robots . . . . . . . . . . . . . . . 10 German I. Parisi, Johannes Bauer, Erik Strahl and Stefan Wermter An Intelligent Application Development Platform for Service Robots . . . . 16 Y. Sugawara, T. Morita, S. Saito and T. Yamaguchi Multimodal binding of parameters for task-based robot programming based on semantic descriptions of modalities and parameter types . . . . . . . . . . . . . 22 Alexander Perzylo, Nikhil Somani, Stefan Profanter, Markus Rickert and Alois Knoll Connecting natural language to task demonstrations and low-level control of industrial robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Maj Stenmark, Jacek Malec Perceptive Parallel Processes Coordinating Geometry and Texture . . . . . . 30 Marco A. Gutierrez, Rafael E. Banchs and Luis F. D'Haro NVIDIA Jetson for Embedded How high-performance and low-energy computing systems for deep learning and computer vision can help Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Serge Palaric Robotic Vision: Understanding Improves the Geometric Accuracy . . . . . . 38 Javier Civera SP1: Stereo Vision in Real Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Konstantin Schauwecker