An accurate motion model is an important component in modern-day robotic systems, but building such a model for a complex system often requires an appreciable amount of manual effort. In this paper we present a motion model representation, the Dynamic Gaussian Mixture Model (DGMM), that alleviates the need to manually design the form of a motion model, and provides a direct means of incorporating auxiliary sensory data into the model. This representation and its accompanying algorithms are validated experimentally using an 8-legged kinematically complex robot, as well as a standard benchmark dataset. The presented method not only learns the robot's motion model, but also improves the model's accuracy by incorporating information about the terrain surrounding the robot.
@article{arxiv.1005.5035,
title = {Dynamic Motion Modelling for Legged Robots},
author = {Mark Edgington and Yohannes Kassahun and Frank Kirchner},
journal= {arXiv preprint arXiv:1005.5035},
year = {2010}
}