Joint Angle Error Reduction for Humanoid Robots Using Dynamics Learning Tree

Abstract

In this paper, we discuss two problems with joints of low-cost humanoid robots.

The first problem is communication errors occurring in angle sensors. We propose a method of compensating for the sensor values by using estimated sensor values by learning the corresponding relationships between the command and sensor values.

Second, there are errors between the command and sensor values. The degree of such errors in a robot arm is affected by both gravity and joint-motion directions. By learning the corresponding relationships between these two factors and the errors, we can estimate these errors and use this estimation to reduce motion error. One of the distinguishing points of the proposed methods is that these two problems are solved by adaptive learning that works under the background system of a moving robot. Another distinguishing point is that the proposed method adapts to the specifications of a robot’s joints regardless of intensive a priori knowledge about the specifications.

From experimental results, we found that it is possible to infer the necessary value to compensate the sensor values that occur in the event of communication error. Moreover, by estimating the error between the command and sensor values and using this estimation to reduce the error, we succeeded in reducing the error in joint angle.