ラズパイ上でリアルタイム学習する歩行ロボット
Self learning robot using real-time neural networks
腕付きロボットにニューラルネットを実装し、ラズパイ3上で局所的に学習させて直進歩行を獲得させ、学習率や誤差許容の影響を解析した。
著者: Chirag Gupta, Chikita Nangia, Chetan Kumar
分類: cs.RO, cs.LG
原文アブストラクト
With the advancements in high volume, low precision computational technology and applied research on cognitive artificially intelligent heuristic systems, machine learning solutions through neural networks with real-time learning has seen an immense interest in the research community as well the industry. This paper involves research, development and experimental analysis of a neural network implemented on a robot with an arm through which evolves to learn to walk in a straight line or as required. The neural network learns using the algorithms of Gradient Descent and Backpropagation. Both the implementation and training of the neural network is done locally on the robot on a raspberry pi 3 so that its learning process is completely independent. The neural network is first tested on a custom simulator developed on MATLAB and then implemented on the raspberry computer. Data at each generation of the evolving network is stored, and analysis both mathematical and graphical is done on the data. Impact of factors like the learning rate and error tolerance on the learning process and final output is analyzed.