By Vishnu Nath, Stephen E. Levinson (auth.)
This Springer short examines the combo of laptop imaginative and prescient ideas and computing device studying algorithms precious for humanoid robots to boost “true consciousness.” It illustrates the severe first step in the direction of attaining “deep learning,” lengthy thought of the holy grail for laptop studying scientists around the world. utilizing the instance of the iCub, a humanoid robotic which learns to unravel 3D mazes, the ebook explores the demanding situations to create a robotic which could understand its personal atmosphere. instead of depending completely on human programming, the robotic makes use of actual contact to improve a neural map of its atmosphere and learns to alter the surroundings for its personal profit. those thoughts enable the iCub to adequately clear up any maze, if an answer exists, inside a couple of iterations. With transparent research of the iCub experiments and its effects, this Springer short is perfect for complicated point scholars, researchers and execs all in favour of desktop imaginative and prescient, AI and desktop learning.
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Additional info for Autonomous Robotics and Deep Learning
This would result in an unstable motion of the robot’s hand while solving the maze, making it susceptible to increased hardware wear and tear. A filter can then be applied to the control policy to obtain a smoothed out policy for the maze. The resultant optimal control policy for the maze is shown in Fig. 9 below. It is this output that is being used for the online maze solving by the iCub robot. 2 Closed Loop Test Fig. 6 RGB version of the maze after inverse homography and grid application Fig.
Solving 3D Mazes with Machine Learning: A prelude to deep learning using the iCub Humanoid Robot. Twenty-Eighth AAAI Conference. , & Norvig, P. (2010). Artificial Intelligence, A Modern Approach. New Jersey: Prentice Hall. , & Vernon, G. (2007). The iCub Cognitive Humanoid Robot: An OpenSystem Research Platform for Enactive Cognition. In 50 years of artificial intelligence (pp. 358–369). Berlin Heidelburg: Springer Berlin Heidelberg. , & Buffet, O. (2010). Markov Decision Processes in Artificial Intelligence.
8 Resultant control policy Fig. 2 Closed Loop Test 53 The following set of figures show the entire actual sequence of solving this particular maze as seen from the iCub’s right eye, in RGB view. Each sequence consists of the current maze in RGB form, the Raster Scan visualization of the position of the ball with respect to the four corners, and lastly the position of the ball in the maze with respect to the grid with the next direction to be applied indicated by the arrow inside the circle.
Autonomous Robotics and Deep Learning by Vishnu Nath, Stephen E. Levinson (auth.)