Robot Weightlifting by Direct Policy Search
Abstract
This paper describes a method for structuring a robot motor learning task. By designing a suitably parameterized policy, we show that a simple search algorithm, along with biologically motivated con-straints, offers an effective means for motor skill acquisition. The framework makes use of the robot counterparts to several elements found in human motor learning: imitation, equilibrium-point con-trol, motor programs, and synergies. We demon-strate that through learning, coordinated behavior emerges from initial, crude knowledge about a dif-ficult robot weightlifting task. 1
Cite
Text
Rosenstein and Barto. "Robot Weightlifting by Direct Policy Search." International Joint Conference on Artificial Intelligence, 2001.Markdown
[Rosenstein and Barto. "Robot Weightlifting by Direct Policy Search." International Joint Conference on Artificial Intelligence, 2001.](https://mlanthology.org/ijcai/2001/rosenstein2001ijcai-robot/)BibTeX
@inproceedings{rosenstein2001ijcai-robot,
title = {{Robot Weightlifting by Direct Policy Search}},
author = {Rosenstein, Michael T. and Barto, Andrew G.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2001},
pages = {839-846},
url = {https://mlanthology.org/ijcai/2001/rosenstein2001ijcai-robot/}
}