Learning inverse kinematics and dynamics of a robotic manipulator using generative adversarial networks

Obtaining inverse kinematics and dynamics of a robotic manipulator is often crucial for robot control. Analytical models are typically used to approximate real robot systems, and various controllers have been designed on top of the analytical model to compensate for the approximation error. Recently, machine learning techniques have been developed for error compensation, resulting in better performance. Unfortunately, combining a learned compensator with an analytical model makes the designed controller redundant and computationally expensive. Also, general machine learning techniques require a lot of data to perform the training process and approximation, especially in solving high dimensional problems. As a result, state-of-the-art machine learning applications are either expensive in terms of computation and data collection, or limited to a local approximation for a specific task or routine. In order to address the high dimensionality problem in learning inverse kinematics and dynamics, as well as to make the training process more data efficient, this paper presents a novel approach using a series of modified Generative Adversarial Networks (GANs). Namely, we use Conditional GANs (CGANs), Least Squares GANs (LSGANs), Bidirectional GANs (BiGANs) and Dual GANs(DualGANs). We trained and tested the proposed methods using real-world data collected from two types of robotic manipulators, a MICO robotic manipulator and a Fetch robotic manipulator. The data input to the GANs was obtained using a sampling method applied to the real data. The proposed approach enables approximating the real model using limited data without compromising the performance and accuracy. The proposed methods were tested in real-world experiments using unseen trajectories to validate the "learned" approximate inverse kinematics and inverse dynamics as well as to demonstrate the capability and effectiveness of the proposed algorithm over existing analytical models. (C) 2019 Elsevier B.V. All rights reserved.