Rich periodic motor skills on humanoid robots: Riding the pedal racer

Just as their discrete counterparts, periodic or rhythmic dynamic motion primitives allow easily modulated and robust motion generation, but for periodic tasks. In this paper we present an approach for modulating periodic dynamic movement primitives based on force feedback, allowing for rich motor behavior and skills. We propose and evaluate the combination of feedback and learned feed-forward terms to fully adapt the motions of a robot in order to achieve a desired force interaction with the environment. For the learning we employ the notion of repetitive control, which can effectively minimize the error of behavior towards a given reference. To demonstrate the approach, we show results of simulated and real world experiments on a compliant humanoid robot COMAN. We show the initial results of utilizing the approach to control a pedal-racer, a demanding balance toy best described as a hybrid between a skateboard and a bicycle. © 2014 IEEE.


Publié dans:
Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), 2326-2332
Présenté à:
2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 31 May - 7 June 2014
Année
2014
Publisher:
IEEE
Laboratoires:




 Notice créée le 2014-12-10, modifiée le 2019-04-16

Postprint:
Télécharger le document
PDF

Évaluer ce document:

Rate this document:
1
2
3
 
(Pas encore évalué)