New PDF release: Autonomous Robotics and Deep Learning

By Vishnu Nath, Stephen E. Levinson (auth.)

ISBN-10: 3319056026

ISBN-13: 9783319056029

ISBN-10: 3319056034

ISBN-13: 9783319056036

This Springer short examines the mix of desktop imaginative and prescient innovations and computer studying algorithms worthwhile for humanoid robots to increase “true consciousness.” It illustrates the severe first step in the direction of attaining “deep learning,” lengthy thought of the holy grail for computer studying scientists world wide. utilizing the instance of the iCub, a humanoid robotic which learns to resolve 3D mazes, the ebook explores the demanding situations to create a robotic which may understand its personal atmosphere. instead of depending completely on human programming, the robotic makes use of actual contact to advance a neural map of its atmosphere and learns to alter the surroundings for its personal gain. those suggestions enable the iCub to adequately remedy any maze, if an answer exists, inside of a number of iterations. With transparent research of the iCub experiments and its effects, this Springer short is perfect for complex point scholars, researchers and pros excited by laptop imaginative and prescient, AI and laptop learning.

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Extra resources for Autonomous Robotics and Deep Learning

Sample text

3) below (Russell and Norvig 2010). 3) may seem very subtle at first, there is a pretty significant difference between Q-learning and the SARSA algorithm. The SARSA algorithm actually waits until an action is taken and then updates the Q-value for that action. Simply put, if a greedy agent that always takes the action with the best Q-value is required, Q-learning is the algorithm to use. However, if exploration of the state space is required, SARSA is the algorithm that offers a lot more advantages.

6, the 32 Â 32 resolution would be able to determine the location of the ball with respect to the maze. For the purposes of this experiment, there is no anticipated loss of information by using this resolution. Upon testing the robot to solve the maze using the 32 Â 32 resolution, it was determined that the robot was able to solve the maze online. As a result, it was decided to use a grid of size 32 Â 32 to handle the video feed for online analysis. 4 Online Analysis Once the optimal control policy has been obtained with the offline analysis of multiple mazes, as part of the training set, the iCub can solve any maze given to it.

6, the 32 Â 32 resolution would be able to determine the location of the ball with respect to the maze. For the purposes of this experiment, there is no anticipated loss of information by using this resolution. Upon testing the robot to solve the maze using the 32 Â 32 resolution, it was determined that the robot was able to solve the maze online. As a result, it was decided to use a grid of size 32 Â 32 to handle the video feed for online analysis. 4 Online Analysis Once the optimal control policy has been obtained with the offline analysis of multiple mazes, as part of the training set, the iCub can solve any maze given to it.

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Autonomous Robotics and Deep Learning by Vishnu Nath, Stephen E. Levinson (auth.)


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