Derivative-free online learning of inverse dynamics models
Mitsubishi Electric Research Labs (MERL)（三菱电机研究实验室（MERL））；University of Padova（帕多瓦大学）；Massachusetts Institute of Technology（麻省理工学院）；Istituto Italiano di Tecnologia（意大利理工学院）
Diego Romeres, Mattia Zorzi, Raffaello Camoriano, Silvio Traversaro, Alessandro Chiuso
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 of the previous two classes) are placed in a common framework. While model classes used in the literature typically exploit joint velocities and accelerations, which need to be approximated resorting to numerical differentiation schemes, in this paper a new `derivative-free' framework is proposed that does not require this preprocessing step. An extensive experimental study with real data from the right arm of the iCub robot is presented, comparing different model classes and estimation procedures, showing that the proposed `derivative-free' methods outperform existing methodologies.
Online semi-parametric learning for inverse dynamics modeling
University of Padova（帕多瓦大学）；Istituto Italiano di Tecnologia（意大利技术研究院）；Massachusetts Institute of Technology（麻省理工学院）；Università degli Studi di Genova（热那亚大学）
An Online-Learning Approach to Inverse Optimization
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大大学埃尔兰根-纽伦堡分校）；Technische Universität Berlin（柏林工业大学）；Zuse-Institut Berlin（柏林祖斯研究所）；Fraunhofer Institut für Integrierte Schaltungen IIS（弗劳恩霍夫集成电路研究所IIS）
Decentralized Online Learning for Random Inverse Problems Over Graphs
No.2 High School of East China Normal University（华东师范大学第二附属中学）；School of Mathematical Sciences, East China Normal University（华东师范大学数学科学学院）；Key Laboratory of Management, Decision and Information Systems, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院系统科学研究所管理、决策与信息系统重点实验室）；School of Mathematical Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）
School of Mathematical Sciences, Nankai University, Tianjin, China（南开大学数学科学学院）；Courant Institute, New York University, NY（纽约大学Courant研究所）；School of Mathematical Sciences, Peking University, Beijing, China（北京大学数学科学学院）；Beijing International Center for Mathematical Research, Center for Machine Learning Research, Peking University, Beijing, China（北京大学北京国际数学研究中心、机器学习研究中心）
