Learning Basis Skills by Autonomous Segmentation of Humanoid Motion Trajectories
Författare
Summary, in English
Manipulation tasks are characterized by continuous motion trajectories containing a set of key phases. In this paper, we propose a probabilistic method to autonomously segment the motion trajectories for estimating the key phases embedded in such a task. The autonomous segmentation process relies on principal component analysis to adaptively project into one of the low-dimensional subspaces, in which a Gaussian mixture model is learned based on Bayesian information criterion and expectation-maximization algorithms. The basis skills are estimated by a set of Gaussians approximating quasi-linear key phases, and those times spent calculated from the segmentation points between two consecutive Gaussians representing the local changes of dynamics and directions of the trajectories. The basis skills are then used to build novel motion trajectories with possible motion alternatives and optional parts. After sequentially reorganizing the basis skills, a Gaussian mixture regression process is used to retrieve smooth motion trajectories. Two experiments are presented to demonstrate the capability of the autonomous segmentation approach.
Avdelning/ar
Publiceringsår
2012
Språk
Engelska
Sidor
112-119
Publikation/Tidskrift/Serie
12th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2012
Fulltext
Länkar
Dokumenttyp
Konferensbidrag
Förlag
IEEE - Institute of Electrical and Electronics Engineers Inc.
Ämne
- Control Engineering
Conference name
12th IEEE-RAS International Conference on Humanoid Robots
Conference date
2012-11-29
Conference place
Osaka, Japan
Status
Published
Projekt
- ROSETTA
- LU Robotics Laboratory
- LCCC
- Intelligent Networked RObotics SYstems with reconfigurable exogenous system sensing
Forskningsgrupp
- LCCC
ISBN/ISSN/Övrigt
- ISSN: 2164-0572