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Nissan’s Robots: They’re Really Fish. CHIBA, Japan -- If we can get drivers to behave like smelt, we can start cutting gas. That was one of the messages from Nissan at Ceatec, the large technology trade show taking place this week outside of Tokyo. Fish swimming in schools maintain a relatively constant distance with the fish in front of it and any swimming on the side. As a result, they don't bash into each other and move along at an orderly pace. By putting sensors in a car that can detect other cars around it and communicate with them, traffic accidents as well as jackrabbit starts, lane shifting and traffic jams could be reduced, said Minoru Shinohara, senior vice president in the technology development division at Nissan.

Ultimately, it could also curb gas consumption. "If we apply the rules of fish congestion can be avoided," he said. "There is very flexible movement in the group. " Those robots, called Eporo demonstrate the concept (see video). Car movement, of course, is a little more challenging. Fuel cells? Machine learning autonomous robots. Autonomous Robots, Volume 30. Special Issue: Robotics: Science and Systems Shinya Aoi, Kazuo Tsuchiya: Generation of bipedal walking through interactions among the robot dynamics, the oscillator dynamics, and the environment: Stability characteristics of a five-link planar biped robot. 123-141Cristina Urdiales, Jose Manuel Peula, Manuel Fernández-Carmona, Cristian Barrué, Eduardo J. Pérez, Isabel Sánchez-Tato, J. C. del Toro, Francesco Galluppi, Ulises Cortés, Roberta Annichiaricco, Carlo Caltagirone, Francisco Sandoval Hernández: A new multi-criteria optimization strategy for shared control in wheelchair assisted navigation. 179-197Malte Schilling: Universally manipulable body models - dual quaternion representations in layered and dynamic MMCs. 399-425Jung Hoon Kim, Jung-Yup Kim, Jun-Ho Oh: Adaptive walking pattern generation and balance control of the passenger-carrying biped robot, HUBO FX-1, for variable passenger weights. 427-443.

Abstract. Autonomous Robots: Special Issue on Robot Learning - Research - Intelligent Autonomous Systems - Informatik - TU Darmstadt. Creating autonomous robots that can learn to act in unpredictable environments has been a long standing goal of robotics, artificial intelligence, and the cognitive sciences. In contrast, current commercially available industrial and service robots mostly execute fixed tasks and exhibit little adaptability. To bridge this gap, machine learning offers a myriad set of methods some of which have already been applied with great success to robotics problems. Machine learning is also likely play an increasingly important role in robotics as we take robots out of research labs and factory floors, into the unstructured environments inhabited by humans and into other natural environments. To carry out increasingly difficult and diverse sets of tasks, future robots will need to make proper use of perceptual stimuli such as vision, lidar, proprioceptive sensing and tactile feedback, and translate these into appropriate motor commands.

Andrew Y. Machine learning « Autonomous Robots Blog. Robots are portrayed as tomorrows helpers, be it in schools, hospitals, workplaces or homes. Unfortunately, such robots won’t be truly useful out-of-the-box because of the complexity of real-world environments and tasks. Instead, they will need to learn how to interact with objects in their environment to produce a desired outcome (affordance learning). For this purpose, robots can explore the world while using machine learning techniques to update their knowledge. However, the learning process is sometimes saturated with examples of objects, actions and effects that won’t help the robot in its purpose. In these cases, humans or other social partners can help direct robot learning (social learning). Most studies have focussed on scenarios where a teacher demonstrates how to correctly do a task.

This approach, while being very efficient, typically means that the teacher needs to take time to train the robot, which can be burdensome. Related posts. Stefan-Schaal-Humanoids2010.pdf (application/pdf Object) Colt.pdf (application/pdf Object) Machine learning autonomous robots. Fulltext.pdf (application/pdf Object) Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focuses on the information management aspects of systems with several components working together towards a common goal; Multiagent Systems (MAS) deals with behavior management in collections of several independent entities, or agents.

This survey of MAS is intended to serve as an introduction to the field and as an organizational framework. A series of general multiagent scenarios are presented. For each scenario, the issues that arise are described along with a sampling of the techniques that exist to deal with them. Programming Game AI By Example - Mat Buckland.

Man Versus Machine: Kasparov Versus Deep Blue - Raymond Keene, Tony Buzan, David Goodman. Machine Learning: An Algorithmic Perspective - Stephen Marsland. Traditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.

Theory Backed up by Practical Examples The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. Highlights a Range of Disciplines and Applications.

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