Three-dimensional neural net for learning visuomotor coordination of a robot arm

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Deep learning in neural networks: An overview (2014, background): “Many papers of the previous millennium, however, were about unsupervised learning (UL) without a teacher (e.g., Hebb, 1949; von der Malsburg, 1973; Kohonen, 1972, 1982, 1988; Willshaw and von der Malsburg, 1976; Grossberg, 1976a,b; Watanabe, 1985; Rumelhart and Zipser, 1986; Pearlmutter and Hinton, ”
Learning Forward Models for the Operational Space Control of Redundant Robots (2010, methodology): “Based on self-organising maps (Kohonen, 2001), Martinetz et al (1990b,a); Walter and Schulten (1993) learn a mapping between end eﬀector position measured by two cameras and joint positions on a three degrees of freedom robot.”
On-line regression algorithms for learning mechanical models of robots: A survey (2011, background): “[63] use soms to learn a mapping between the three-dimensional”
Self-Organizing Feature Maps for Modeling and Control of Robotic Manipulators (2003, methodology): “Ritter et al. (1989), Martinetz et al. (1990), and Martinetz and Schulten (1993) have used a three-dimensional lattice, each node in the lattice corresponding to a neuron unit, to learn the nonlinear transformation θ(V) which specifies the joint angles that takes the tip of a 3-DOF robot arm to the…”