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Capturing Skill State in Curriculum Learning for Human Skill Acquisition.
Keya
Ghonasgi, Reuth Mirsky, Sanmit
Narvekar, Bharath Masetty, Adrian M. Haith, Peter Stone, and Ashish D.
Deshpande.
In International Conference on Intelligent Robots and Systems (IROS), September 2021.
Video
presentation
Humans learn complex motor skills with practice and training. Though the learning process is not fully understood, several theories from motor learning, neuroscience, education, and game design suggest that curriculum-based training may be the key to efficient skill acquisition. However, designing such a curriculum and understanding its effects on learning are challenging problems. In this paper, we define the Human-skill Curriculum Markov Decision Process (H-CMDP) to systematize the design of training protocols. We also identify a vocabulary of performance features to enable the approximation for a human's skill level across a variety of cognitive and motor tasks. A novel task domain is introduced as a testbed to evaluate the effectiveness of our approach. Human subject experiments show that (1) participants can learn to improve their performance in tasks within this domain, (2) the learning is quantifiable via our performance features, and (3) the domain is flexible enough to create distinct levels of difficulty. The long-term goal of this work is to systematize the process of curriculum-based training toward the design of protocols for robot-mediated rehabilitation.
@InProceedings{IROS2021-REUTH,
author = {Keya Ghonasgi and Reuth Mirsky and Sanmit Narvekar and Bharath Masetty and Adrian M. Haith and Peter Stone and Ashish D. Deshpande},
title = {Capturing Skill State in Curriculum Learning for Human Skill Acquisition},
booktitle = {International Conference on Intelligent Robots and Systems (IROS)},
location = {Virtual},
month = {September},
year = {2021},
abstract = {
Humans learn complex motor skills with practice and
training. Though the learning process is not fully understood,
several theories from motor learning, neuroscience, education,
and game design suggest that curriculum-based training may be
the key to efficient skill acquisition. However, designing
such a curriculum and understanding its effects on learning
are challenging problems. In this paper, we define the
Human-skill Curriculum Markov Decision Process (H-CMDP) to
systematize the design of training protocols. We also identify
a vocabulary of performance features to enable the
approximation for a human's skill level across a variety of
cognitive and motor tasks. A novel task domain is introduced
as a testbed to evaluate the effectiveness of our
approach. Human subject experiments show that (1) participants
can learn to improve their performance in tasks within this
domain, (2) the learning is quantifiable via our performance
features, and (3) the domain is flexible enough to create
distinct levels of difficulty. The long-term goal of this work
is to systematize the process of curriculum-based training
toward the design of protocols for robot-mediated
rehabilitation.
},
wwwnote = {<a href="https://youtu.be/TI1Zh4L63Xw">Video presentation</a>}
}
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