Model Identification

By J. Hollerbach, W. Khalil, M. Gautier. 2019

197 citations recorded by the source corpus.

How researchers see this paper

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Model-based reinforcement learning with parametrized physical models and optimism-driven exploration (2015, methodology): “System and model identification has been explored extensively in the context of robotics [16], [20].”
Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression (2014, methodology): “Given a training set X and a test set Y , our method for estimating the function F relating observed states xo to desired states xd consists of three consectutive stages performed offline: 1) Data Cleaning 2) Estimation of Rigid Transformation 3) Gaussian Process Regression Our method requires train”
Bootstrapping bilinear models of Simple Vehicles (2015, background): “For a recent review, see (Hollerbach, Khalil, and Gautier, 2008).”
Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems (2016, methodology): “To use these methods, practitioners have to provide models of the environment, calibrate the robot [21], and localize it in the environment [48].”
Identification of consistent standard dynamic parameters of industrial robots (2013, background): “It is known that the dynamic model of any manipulator with n actuators can be linearly written in term of a (n×1) vector of standard parameters $st [1], [13], [14]: ( ) ( ) W F F idm st st st q,q,q, IDM q,q,q (2)”