Online semi-parametric learning for inverse dynamics modeling
This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model. It combines the strength of the parametric and non-parametric modeling. The former exploits the rigid body dynamics equa- tion, while the latter exploits a suitable kernel function. We provide an extensive comparison with other methods from the literature using real data from the iCub humanoid robot. In doing so we also compare two different techniques, namely cross validation and marginal likelihood optimization, for estimating the hyperparameters of the kernel function.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Incremental Semiparametric Inverse Dynamics Learning
This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonp…
Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics
Physical modeling of robotic system behavior is the foundation for controlling many robotic mechanisms to a satisfactory degree. Mechanisms are also typically designed in a way that good model accuracy can be achieved wi…
BIG-bench Machine LearningmodelregressionIterative Semi-parametric Dynamics Model Learning For Autonomous Racing
Accurately modeling robot dynamics is crucial to safe and efficient motion control. In this paper, we develop and apply an iterative learning semi-parametric model, with a neural network, to the task of autonomous racing…
Autonomous RacingDerivative-free online learning of inverse dynamics models
This paper discusses online algorithms for inverse dynamics modelling in robotics. Several model classes including rigid body dynamics (RBD) models, data-driven models and semiparametric models (which are a combination o…
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning
Motivated by the recursive Newton-Euler formulation, we propose a novel cascaded Gaussian process learning framework for the inverse dynamics of robot manipulators. This approach leads to a significant dimensionality red…
Dimensionality ReductionGaussian Processes