Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.

Modelling and Control of Robot Manipulators

https://hal.archives-ouvertes.fr/hal-00629133/document

On-line regression algorithms for learning mechanical models of robots: A survey

In certain cases analytical derivation of physics-based models of robots is difficult or even impossible. A potential workaround is the approximation of robot models from sensor data-streams employing machine learning approaches. In this paper, the inverse dynamics models are learned by employing a novel real-time deep learning algorithm. The algorithm exploits the methods of self-organized learning, reservoir computing and Bayesian inference. It is evaluated and compared to other state of the art algorithms in terms of generalization ability, convergence and adaptability using five datasets gathered from four robots. Results show that the proposed algorithm can adapt to real-time changes of the inverse dynamics model significantly better than the other state of the art algorithms.

Real-time deep learning of robotic manipulator inverse dynamics

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 nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. The result is an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot.

Incremental semiparametric inverse dynamics learning

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 equation, 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.

Online semi-parametric learning for inverse dynamics modeling

Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship of a manipulator, eliminating the need for any knowledge of the system model. Ideally, such algorithms should be able to process large amounts of data in an online and incremental manner, thus allowing the system to adapt to changes in its model structure or parameters. LocallyWeighted Projection Regression (LWPR) and other non-parametric regression techniques have been applied to learn manipulator inverse dynamics. However, a common issue amongst these learning algorithms is that the system is unable to generalize well outside of regions where it has been trained. Furthermore, learning commences entirely from 'scratch,' making no use of any a-priori knowledge which may be available. In this paper, an online, incremental learning algorithm incorporating prior knowledge is proposed. Prior knowledge is incorporated into the LWPR framework by initializing the local linear models with a first order approximation of the available prior information. It is shown that the proposed approach allows the system to operate well even without any initial training data, and further improves performance with additional online training.

/pdf/online-incremental-learning-of-inverse-dynamics-1ibwm99zq2.pdf

Online incremental learning of inverse dynamics incorporating prior knowledge

Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. This paper proposes an approach for online learning of the inverse dynamics model using Gaussian Process Regression. The Sparse Online Gaussian Process (SOGP) algorithm is modified to allow for incremental updates of the model and hyperparameters. The influence of initialization on the performance of the learning algorithms, based on any a-priori knowledge available, is also investigated. The proposed approach is compared to existing learning and fixed control algorithms and shown to be capable of fast initialization and learning rate.

/pdf/online-learning-of-inverse-dynamics-via-gaussian-process-5a5gz8u1b3.pdf

Online learning of inverse dynamics via Gaussian Process Regression

High performance control of robotic systems, including the new generation of humanoid, assistive and entertainment robots, requires adequate knowledge of the dynamics of the system This can be problematic in the presence of modeling uncertainties as the performance of classical, modelbased controllers is highly dependant upon accurate knowledge of the system In addition, future robotic systems such as humanoids are likely to be redundant, requiring a mechanism for redundancy resolution when performing lower degree-of-freedom tasks In this paper, a learning approach to estimating the inverse dynamic equations is presented Locally Weighted Projection Regression (LWPR) is used to learn the inverse dynamics of a manipulator in both joint and task space and the resulting controllers are used to drive a 3 and 4 DOF robot in simulation The performance of the learning controllers is compared to a traditional model based control method and is also shown to be a viable control method for a redundant system

/pdf/learning-inverse-dynamics-for-redundant-manipulator-control-24rvfz8g17.pdf

Learning inverse dynamics for redundant manipulator control

https://ece.uwaterloo.ca/~dkulic/pubs/delaCruzSYROCO2012.pdf

On-line Dynamic Model Learning for Manipulator Control

https://ece.uwaterloo.ca/~dkulic/pubs/SunDeLaCruzIFAC2011.pdf

Joseph Sun de la Cruz

Papers

Online incremental learning of inverse dynamics incorporating prior knowledge

Online learning of inverse dynamics via Gaussian Process Regression

Learning inverse dynamics for redundant manipulator control

On-line Dynamic Model Learning for Manipulator Control

A comparison of classical and learning controllers