6th World Machine Learning and Deep Learning Congress

Robot learns to walk by itself using artificial intelligence. Have you ever wondered how human-like a robot can become? Initially, in the learning phase, a tendon-driving robotic limb undergoes a motor babbling phase where the system attempts random control sequences and gathers the associated kinematics. The input-output data from the motor babbling is fed to a multi-layer perceptron artificial neural network (ANN) to train it. In turn, the trained ANN produces an initial output-input (inverse) map based on the system’s dynamics. The ANN of the inverse map from 6D kinematics to a 3D motor control sequences has three layers and twenty-four nodes total. There are six nodes in the input layer, fifteen nodes in the hidden layer, and three nodes in the output layer. The hyperbolic tangent sigmoid transfer function was used to compute a layer’s output from its net input—well suited for neural networks when velocity is a priority over the precise shape of the transfer function