On-line regression algorithms for learning mechanical models of robots: a survey - Archive ouverte HAL Access content directly
Journal Articles Robotics and Autonomous Systems Year : 2011

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

(1, 2) , (1) , (1)
1
2

Abstract

With the emergence of more challenging contexts for robotics, the mechanical design of robots is becoming more and more complex. Moreover, their missions often involve unforeseen physical interactions with the environment. To deal with these difficulties, endowing the controllers of the robots with the capability to learn a model of their kinematics and dynamics under changing circumstances is becoming mandatory. This emergent necessity has given rise to a significant amount of research in the Machine Learning community, generating algorithms that address more and more sophisticated on-line modeling questions. In this paper, we provide a survey of the corresponding literature with a focus on the methods rather than on the results. In particular, we provide a unified view of all recent algorithms that outlines their distinctive features and provides a framework for their combination. Finally, we give a prospective account of the evolution of the domain towards more challenging questions.
Fichier principal
Vignette du fichier
RAS2010_Sigaud_Salaun_Padois.pdf (215.69 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00629133 , version 1 (05-10-2011)

Identifiers

Cite

Olivier Sigaud, Camille Salaün, Vincent Padois. On-line regression algorithms for learning mechanical models of robots: a survey. Robotics and Autonomous Systems, 2011, 59 (12), pp.1115-1129. ⟨10.1016/j.robot.2011.07.006⟩. ⟨hal-00629133⟩
218 View
1634 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More