TAILIEUCHUNG - Learning Fine Motion in Robotics: Experiments with the Hierarchical Extended Kohonen Map

Technology can be viewed as an activity that forms or changes culture.[11] Additionally, technology is the application of math, science, and the arts for the benefit of life as it is known. A modern example is the rise of communication technology, which has lessened barriers to human interaction and, as a result, has helped spawn new subcultures; the rise of cyberculture has, at its basis, the development of the Internet and the computer.[12] Not all technology enhances culture in a creative way; technology can also help facilitate political oppression and war via tools such as guns. As a cultural activity,. | Learning Fine Motion in Robotics Experiments with the Hierarchical Extended Kohonen Map Cristina Versinof Luca Maria Gambardellaf f IDSIA Corso Elvezia 36 6900 Lugano Switzerland cristina@ http cristina Abstract We present a Hierarchical Extended Kohonen Map HEKM and a planning system which cooperate to solve the robot path finding problem. The HEKM learns to associate appropriate actions to perceptions under the supervision of the planner. First we argue for the utility of using the hierarchical version of the KM instead of the flat KM the HEKM provides a natural and economic representation of the robot s perceptual states. Second we measure the benefits of cooperative learning due to the interaction of neighboring neurons in the HEKM with cooperation learning is slowed down in the short run but the benefits appear later on resulting in a more satisfactory final performance. Third we highlight a beneficial side-effect obtained by transferring motion skill from the planner to the HEKM namely smoothness of motion. 1 Introduction The problem of path finding has attracted considerable attention by robotics research. This is the problem of moving a robot from a starting position to a goal position avoiding collisions against obstacles in the workspace. Moreover the robot path should be as short and smooth as possible. The path is optimal if it is the shortest from the starting position to the goal. Path finders are methods to automatically solve the path finding problem. Traditionally path finders are either model-based or sensor-based. While model-based systems address the path finding problem globally using a model of the workspace sensor-based systems consider it locally and rely on robot sensors to avoid obstacles. Both methods have limitations which are rather complementary. Model-based systems compute optimal free-paths and recover easily from dead-ends but require a complete description of the robot workspace and are computationally .

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