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.

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In International Conference on Intelligent Robots and Systems (IROS), Virtual, September 2021.

@inproceedings{IROS2021-REUTH, title={Capturing Skill State in Curriculum Learning for Human Skill Acquisition}, author={Keya Ghonasgi and Reuth Mirsky and Sanmit Narvekar and Bharath Masetty and Adrian M. Haith and Peter Stone and Ashish D. Deshpande}, booktitle={International Conference on Intelligent Robots and Systems (IROS)}, month={September}, address={Virtual}, url="http://nn.cs.utexas.edu/?IROS2021-REUTH", year={2021} }