The Playful Machine: Theoretical Foundation and Practical by Ralf Der, Georg Martius, Rolf Pfeifer

By Ralf Der, Georg Martius, Rolf Pfeifer

Autonomous robots may possibly develop into our closest partners within the close to destiny. whereas the know-how for bodily construction such machines is already on hand this present day, an issue lies within the iteration of the habit for such advanced machines. Nature proposes an answer: little ones and better animals learn how to grasp their advanced brain-body structures through taking part in. Can this be an alternative for robots? How can a computing device be playful? The booklet presents solutions by means of constructing a common principle---homeokinesis, the dynamical symbiosis among mind, physique, and environment---that is proven to force robots to self- decided, person improvement in a playful and clearly embodiment- similar approach: a dog-like robotic begins fiddling with a barrier, finally leaping or hiking over it; a snakebot develops coiling and leaping modes; humanoids improve mountain climbing behaviors whilst fallen right into a pit, or have interaction in wrestling-like eventualities whilst encountering an opponent. The e-book additionally develops guided self-organization, a brand new strategy that is helping to make the playful machines healthy for gratifying projects within the actual world.

The booklet offers degrees of presentation. scholars and medical researchers attracted to the sector of robotics, self-organization and dynamical structures concept will be chuffed via the in-depth mathematical research of the main, the bootstrapping eventualities, and the rising behaviors. however the e-book also comes with a robotics simulator inviting additionally the non- clinical reader to easily benefit from the significant international of playful machines by means of acting the varied experiments.

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Extra info for The Playful Machine: Theoretical Foundation and Practical Realization of Self-Organizing Robots

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These controllers can be rather complex, in particular they may contain internal states that modify the mapping depending on contexts, see Eqs. 5). In the BARREL case yt ∈ R2 gives the nominal positions of the internal weights on their respective axes. We will use for the moment a very simple controller that will prove sufficient to produce a rolling motion with fixed velocity. The idea is that in the stable rolling mode, the vector of actions yt is to be in a fixed phase relation to the vector of the sensor values xt .

Choose one of the options described at the website to get the software on your computer. html, which when opened in a browser shows all experiment descriptions and provides links to start them. For each simulation a terminal window and a graphical window opens. 1. At the same time the terminal window shows a welcome text and the parameters that are used. While working with the simulator both windows are important. The terminal window allows to check and change parameters via a text-based console, which can be entered by pressing +C in the terminal window.

7, so that the wheel can rotate forward and backward. We may interpret this as a simple rate-coded neuron with tanh activation function and synaptic strength C, the neural network realization, see Sect. 6, of the controller consisting just of this one neuron. Using Eq. 16) in Eq. 2 0 −2 4 −1 3 0 1 y 2 3 4 2 x Fig. 6: Model error of a wheeled robot. A typical trajectory of the robot with the model errors ξ 2 , see Eq. 18). While during the normal drive the error is essentially given by the measuring uncertainties, the error increases drastically at the repeated collisions with the wall since wheels get blocked or may also rotate freely if the robot gets jammed.

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