**AUTHORS:**Yutaka Maeda, Naoyuki Ishibashi

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**ABSTRACT:**
Robots are widely used in many fields. It is important to provide many different methodologies for
robot control. This paper proposes a real time scheme for robots control and learning using recurrent neural
network. We handle a problem to control a position and a trajectory of tip of a Selective Compliance Assembly
Robot Arm(SCARA) robot. We adopt the simultaneous perturbation optimization method as a learning rule of
the recurrent neural networks(RNNs). Then the RNNs have to learn an inverse dynamics of the SCARA robot.
Position and trajectory control of a SCARA robot using RNN are considered. We could confirm that the RNNs
can learn the inverse dynamics and work as a neuro-controller. We describe details of the control scheme. Some
experimental results for these control using an actual SCARA robot are shown

**KEYWORDS:**
Robot control, Learning, Recurrent neural networks, Simultaneous perturbation, SCARA, Inverse
dynamics, Real time control

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