Online Bayesian changepoint detection for articulated motion models
Scott Niekum, Sarah Osentoski, Christopher G. Atkeson, Andrew G. Barto
Abstract
We introduce CHAMP, an algorithm for online Bayesian changepoint detection in settings where it is difficult or undesirable to integrate over the parameters of candidate models. CHAMP is used in combination with several articulation models to detect changes in articulated motion of objects in the world, allowing a robot to infer physically-grounded task information. We focus on three settings where a changepoint model is appropriate: objects with intrinsic articulation relationships that can change over time, object-object contact that results in quasi-static articulated motion, and assembly tasks where each step changes articulation relationships. We experimentally demonstrate that this system can be used to infer various types of information from demonstration data including causal manipulation models, human-robot grasp correspondences, and skill verification tests.
BibTeX
@inproceedings{icra2015_onlinebayesianch,
title = {Online Bayesian changepoint detection for articulated motion models},
author = {Scott Niekum and Sarah Osentoski and Christopher G. Atkeson and Andrew G. Barto},
booktitle = {ICRA 2015},
year = {2015}
}