@Inbook{Havoutis2013,
author="Havoutis, Ioannis
and Ramamoorthy, Subramanian",
editor="Mombaur, Katja
and Berns, Karsten",
title="Motion Generation with Geodesic Paths on Learnt Skill Manifolds",
bookTitle="Modeling, Simulation and Optimization of Bipedal Walking",
year="2013",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="43--51",
abstract="We present a framework for generating motions drawn from parametrized classes of motions and in response to goals chosen arbitrarily from a set. Our framework is based on learning a manifold representation of possible trajectories, from a set of example trajectories that are generated by a (computationally expensive) process of optimization. We show that these examples can be utilized to learn a manifold on which all feasible trajectories corresponding to a skill are the geodesics. This manifold is learned by inferring the local tangent spaces from data. Our main result is that this process allows us to define a flexible and computationally efficient motion generation procedure that comes close to the much more expensive computational optimization procedure in terms of accuracy while taking a small fraction of the time to perform a similar computation.",
isbn="978-3-642-36368-9",
doi="10.1007/978-3-642-36368-9_4",
url="https://doi.org/10.1007/978-3-642-36368-9_4"
}