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

Abstract

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