By James M. Conrad, Jonathan W. Mills (auth.), Andrew Adamatzky, Maciej Komosinski (eds.)

ISBN-10: 1848825293

ISBN-13: 9781848825291

ISBN-10: 1848825307

ISBN-13: 9781848825307

Hopping, hiking and swimming robots, nano-size neural networks, motorless walkers, slime mildew and chemical brains --- this publication bargains precise designs and prototypes of life-like creatures in traditional and hybrid bio-silicon structures. rules and implementations of residing phenomena in non-living substrates solid a colorful photo of cutting-edge advances in versions of synthetic existence.

Focusing on subject matters and components in accordance with non-traditional pondering, and new and rising paradigms in bio-inspired robotics, this e-book has a unifying topic: the layout and real-world implementation of man-made existence robot devices.

Students and researchers will locate this assurance of subject matters comparable to robot strength autonomy, multi-locomotion of robots, biologically encouraged self sufficient robots, evolution in colonies of robot bugs, neuromorphic analog units, self-configurable robots, and chemical and organic controllers for robots, will significantly increase their realizing of the problems keen on the improvement of not-traditional platforms on the cusp of man-made existence and robotics.

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More specif cally, while the dynamic legged locomotion research were limited to only periodic behavior patterns, we will explore the mechanisms in which the rules of motor control can be generated from the physical interactions in the legged robotic systems. Note that this article shows only the important aspect of the case studies in order to discuss conceptual issues. More technical details can be found in the corresponding publications [6, 11]. 2 Learning from Delayed Reward Physical dynamic interactions play an important role not only for the repetitive behavior patterns such as walking and running on a f at terrain, but also for the resilient behaviors such as high jumps and kicking a ball.

Stable running with segmented legs. International Journal of Robotics Research 27, 919–934 (2008) 24. : Reinforcement learning. MIT Press, Cambridge, MA (2000) 2 Learning Legged Locomotion 33 25. : Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biological Cybernetics 65, 147–159 (1991) 26. : Actuating a simple 3D passive dynamic walker. 1 Introduction One of the key characteristics of animals, perhaps the most impressive, is their ability to move.

The importance of such elastic properties in muscle–tendon systems has been long recognized in biomechanics, where it has a particular significa ce in theoretical models for the locomotion of legged animals [2, 19]. 2 Learning Legged Locomotion 25 (a) (b) Fig. 3 (a) Learning results of motor control in simulation. The optimized sequence of motor frequencies exhibits 12 leg steps successfully traveling through a rough terrain. (b) Time-series photographs of the robot hopping over the steps. The motor control parameter was f rst optimized in simulation and transferred to the robot for the real-world experiment As in the typical reinforcement learning, this learning process utilizes a discount factor γ , which infl ences the selection of action with respect to the prior action.

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Artificial life models in hardware by James M. Conrad, Jonathan W. Mills (auth.), Andrew Adamatzky, Maciej Komosinski (eds.)

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