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Regular version of the site

Book chapter

Artificial Intelligence for Prosthetics: Challenge Solutions

P. 69-128.
Shpilman A., Kidzinski L., Ong C., Mohanty S. P., Hicks J., Carroll S., Zhou B., Zeng H., Wang F., Lian R., Tian H., Jaskowski W., Garrett A., Lykkebo O. R., Toklu N. E., Shyam P., Srivastava R. K., Kolesnikov S., Hrinchuk O., Pechenko A., Mattias L., Wang Z., Hu X., Hu Z., Qiu M., Huang J., Sosin I., Svidchenko O., Malysheva A., Kudenko D., Rane L., Bhatt A., Wang Z., Qi P., Yu Z., Peng P., Yuan Q., Li W., Tian Y., Yang R., Ma P., Khadka S., Majumdar S., Dwiel Z., Liu Y., Tumer E., Watson J., Salathe M., Levine S., Delp S.

In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector. Top participants described their algorithms in this paper. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each team implemented different modifications of the known algorithms by, for example, dividing the task into subtasks, learning low-level control, or by incorporating expert knowledge and using imitation learning.

In book

Edited by: S. Escalera, R. Herbrich. Springer, Cham, 2019.