Visual Perception for Manipulation and Imitation in Humanoid Robots. This item is unavailable.Visual Perception for Manipulation and Imitation in Humanoid Robots

In recent yearswe haveseen considerableadvances in the development of - manoid robots, that is robots with an anthropomorphic design. Such robots should be capable of autonomously performing tasks for their human users in changing environments by adapting to these and to the circumstances at hand. To do so, they as well as any kind of autonomous robot need to have some way of understanding the world around them. We humans do so by our senses, both our far senses vision and hearing (smelling too) and our near senses touch and taste. Vision plays a special role in the way it simulta- ously tells us "where" and "what" in a direct way. It is therefore an accepted factthatto developautonomousrobots,humanoidornot,itisessentialto- clude competent systems for visual perception. Such systems should embody techniques from the ?eld of computer vision, in which sophisticated com- tational methods for extracting information from visual imagery have been developed over a number of decades. However, complete systems incorpor- ing such advanced techniques, while meeting the requirements of real-time processing and adaptivity to the complexity that even our everyday envir- ment displays, are scarce. The present volume takes an important step for ?lling this gap by presenting methods and a system for visual perception for a humanoid robot with speci?c applications to manipulation tasks and to how the robot can learn by imitating the human.

The development of humanoid robots is one of the most challenging research fields within robotics. One of the crucial capabilities of such a humanoid is the ability to visually perceive its environment.

The present monograph deals with visual perception for the intended applications manipulation and imitation, supporting higher-level cognition. In particular, stereo-based methods and systems for object recognition and 6 DoF pose estimation as well as for markerless human motion capture are presented. After an extensive presentation of the state of the art in these areas, three real-time systems that have been developed by the author are presented in great detail: object recognition and pose estimation for textured and for single-colored objects, and a markerless human motion capture system.

As only sensor a stereo camera system is used. All experiments have been performed using the humanoid robot ARMAR-III. The systems presented in this monograph are successfully applied for various research activities in the context of humanoid robotics at the University of Karlsruhe, including manipulation, imitation, visual servoing, motion planning, and higher-level planning.