TY - GEN
T1 - Capturing Skill State in Curriculum Learning for Human Skill Acquisition∗
AU - Ghonasgi, Keya
AU - Mirsky, Reuth
AU - Narvekar, Sanmit
AU - Masetty, Bharath
AU - Haith, Adrian M.
AU - Stone, Peter
AU - Deshpande, Ashish D.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85124373754&partnerID=8YFLogxK
U2 - 10.1109/IROS51168.2021.9636850
DO - 10.1109/IROS51168.2021.9636850
M3 - Conference contribution
AN - SCOPUS:85124373754
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 771
EP - 776
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
PB - Institute of Electrical and Electronics Engineers
T2 - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Y2 - 27 September 2021 through 1 October 2021
ER -