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Artificial Intelligence for Prosthetics — challenge solutions

Łukasz Kidziński Carmichael Ong Sharada Prasanna Mohanty Jennifer Hicks
Sean F. Carroll Bo Zhou Hongsheng Zeng Fan Wang
Rongzhong Lian Hao Tian Wojciech Jaśkowski Garrett Andersen Odd Rune Lykkebø Nihat Engin Toklu Pranav Shyam Rupesh Kumar Srivastava Sergey Kolesnikov Oleksii Hrinchuk Anton Pechenko Mattias Ljungström Zhen Wang Xu Hu Zehong Hu Minghui Qiu Jun Huang Aleksei Shpilman Ivan Sosin Oleg Svidchenko Aleksandra Malysheva Daniel Kudenko Lance Rane Aditya Bhatt Zhengfei Wang Penghui Qi Zeyang Yu Peng Peng Quan Yuan Wenxin Li Yunsheng Tian Ruihan Yang Pingchuan Ma Shauharda Khadka Somdeb Majumdar Zach Dwiel Yinyin Liu Evren Tumer Jeremy Watson Marcel Salathé Sergey Levine Scott Delp
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DATE PUBLISHED
07 Feb 2019
VENUE
Springer
Citations
12

Abstract

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 were invited to describe their algorithms. In this work, we describe the challenge and present thirteen solutions that used deep reinforcement learning approaches. 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.

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