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

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

This Springer short examines the combo of machine imaginative and prescient recommendations and desktop studying algorithms useful for humanoid robots to strengthen “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 resolve 3D mazes, the publication explores the demanding situations to create a robotic that could understand its personal atmosphere. instead of depending exclusively on human programming, the robotic makes use of actual contact to increase a neural map of its surroundings and learns to alter the surroundings for its personal profit. those suggestions let the iCub to thoroughly remedy 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 pros all in favour of desktop imaginative and prescient, AI and computer learning.

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Extra resources for Autonomous Robotics and Deep Learning

Sample text

5 Labelled start and end points (Nath and Levinson 2014) to rest at one of the corners of the maze. After a sufficiently long run, time value iteration converges and an optimal policy is obtained. Filtering of the optimal policy provides more general control domains. The final filtered control policy corresponding to the n  n is then saved for online control. 4 shows the resultant path after the second round of RasterScan, along with a grid. The grid is necessary for the online analysis of any given maze because it helps to derive the optimum rules for controlling the motion of the ball, and can be localized on a regional basis.

8 below. As can be seen from Fig. 8, the control policy is scattered across various neighboring points. 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.

2013a). Learning to Fire at Targets by an iCub Humanoid Robot. AAAI Spring Symposium. Palo Alto: AAAI. , & Levinson, S. (2013b). Usage of computer vision and machine learning to solve 3D mazes. Urbana: University of Illinois at Urbana-Champaign. , & Levinson, S. (2014). 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.

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