• Sorted by Date • Classified by Publication Type • Classified by Topic • Sorted by First Author Last Name •
Adam Setapen, Michael Quinlan, and Peter Stone. MARIOnET: Motion Acquisition for Robots through Iterative Online Evaluative Training. In Ninth International Conference on Autonomous Agents and Multiagent Systems - Agents Learning Interactively from Human Teachers Workshop (AAMAS - ALIHT), May 2010.
supplemental video cited in the paper.
[PDF]533.2kB [postscript]9.7MB
Although machine learning has improved the rate and accuracy at which robots are able to learn, there still exist tasks for which humans can improve performance significantly faster and more robustly than computers. While some ongoing work considers the role of human reinforcement in intelligent algorithms, the burden of learning is often placed solely on the computer. These approaches neglect the expressive capabilities of humans, especially regarding our ability to quickly refine motor skills. In this paper, we propose a general framework for Motion Acquisition for Robots through Iterative Online Evaluative Training (MARIOnET). Our novel paradigm centers around a human in a motion-capture laboratory that “puppets” a robot in real-time. This mechanism allows for rapid motion development for different robots, with a training process that provides a natural human interface and requires no technical knowledge. Fully implemented and tested on two robotic platforms (one quadruped and one biped), this paper demonstrates that MARIOnET is a viable way to directly transfer human motion skills to robots.
@InProceedings{AAMASWS10-setapen,
author = "Adam Setapen and Michael Quinlan and Peter Stone",
title = "MARIOnET: Motion Acquisition for Robots through Iterative Online Evaluative Training",
booktitle = "Ninth International Conference on Autonomous Agents and Multiagent Systems - Agents Learning Interactively from Human Teachers Workshop (AAMAS - ALIHT)",
location = "Toronto, Canada",
month = "May",
year = "2010",
abstract = {
Although machine learning has improved the rate and accuracy at which
robots are able to learn, there still exist tasks for which humans can
improve performance significantly faster and more robustly than
computers. While some ongoing work considers the role of human
reinforcement in intelligent algorithms, the burden of learning is often
placed solely on the computer. These approaches neglect the expressive
capabilities of humans, especially regarding our ability to quickly
refine motor skills. In this paper, we propose a general framework for
Motion Acquisition for Robots through Iterative Online Evaluative
Training (MARIOnET). Our novel paradigm centers around a human in a
motion-capture laboratory that ``puppets'' a robot in real-time. This
mechanism allows for rapid motion development for different robots, with
a training process that provides a natural human interface and requires
no technical knowledge. Fully implemented and tested on two robotic
platforms (one quadruped and one biped), this paper demonstrates that
MARIOnET is a viable way to directly transfer human motion skills to
robots.
},
wwwnote={<a href="http://www.cs.utexas.edu/~AustinVilla/?p=research/marionet">supplemental video</a> cited in the paper.},
}
Generated by bib2html.pl (written by Patrick Riley ) on Tue Nov 19, 2024 10:29:30