Sponsor:
European Community's Seventh Framework Program This work was supported in part by the FCT Programa
Operacional Sociedade de Informaçao (POSC) in the
frame of QCA III, PTDC/EEA-ACR/70174/2006 project and
SFRH/BPD/48857/2008 grant; and in part by the EU Project
Handle (EU-FP7-ICT-231640).
We present an active learning algorithm for the
problem of body schema learning, i.e. estimating a kinematic
model of a serial robot. The learning process is done online
using Recursive Least Squares (RLS) estimation, which outperforms
gradient methods usuallyWe present an active learning algorithm for the
problem of body schema learning, i.e. estimating a kinematic
model of a serial robot. The learning process is done online
using Recursive Least Squares (RLS) estimation, which outperforms
gradient methods usually applied in the literature.
In addiction, the method provides the required information to
apply an active learning algorithm to find the optimal set of
robot configurations and observations to improve the learning
process. By selecting the most informative observations, the
proposed method minimizes the required amount of data.
We have developed an efficient version of the active learning
algorithm to select the points in real-time. The algorithms have
been tested and compared using both simulated environments
and a real humanoid robot.[+][-]
Description:
Proceedings of: 2010 IEEE International Conference on Robotics and Automation (ICRA'10), May 3-8, 2010, Anchorage (Alaska, USA)