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Inverse Kinematics (IK) solves the problem of mapping from the Cartesianspace to the joint configuration space of a robotic arm. It has a wide range ofapplications in areas such as computer graphics, protein structure prediction,and robotics. With the vast advances of artificial neural networks (NNs), manyresearchers recently turned to data-driven approaches to solving the IKproblem. Unfortunately, NNs become inadequate for robotic arms with redundantDegrees-of-Freedom (DoFs). This is because such arms may have multiple anglesolutions to reach the same desired pose, while typical NNs only implementone-to-one mapping functions, which associate just one consistent output for agiven input. In order to train usable NNs to solve the IK problem, mostexisting works employ customized training datasets, in which every desired poseonly has one angle solution. This inevitably limits the generalization andautomation of the proposed approaches. This paper breaks through at two fronts:(1) a systematic and mechanical approach to training data collection thatcovers the entire working space of the robotic arm, and can be fully automatedand done only once after the arm is developed; and (2) a novel NN-basedframework that can leverage the redundant DoFs to produce multiple anglesolutions to any given desired pose of the robotic arm. The latter isespecially useful for robotic applications such as obstacle avoidance andposture imitation.

Automating the Learning of Inverse Kinematics for Robotic Arms with Redundant DoFs