用于假肢的人工智能——挑战赛解决方案
机器学习
2019-02-08 v1 机器人学
机器学习
摘要
在 NeurIPS 2018 人工智能用于假肢挑战赛中，参赛者的任务是为肌肉骨骼模型构建控制器，以匹配给定的时变速度向量。排名靠前的参赛者受邀描述其算法。在本工作中，我们介绍了该挑战赛并展示了十三种采用深度强化学习方法的解决方案。许多方案使用了类似的松弛与启发式方法，例如奖励塑形、帧跳过、动作空间离散化、对称性和策略混合。然而，各团队通过对已知算法的不同修改来实现，例如将任务划分为子任务、学习底层控制，或融入专家知识并使用模仿学习。
引用
@article{arxiv.1902.02441,
title = {Artificial Intelligence for Prosthetics - challenge solutions},
author = {Łukasz Kidziński and Carmichael Ong and Sharada Prasanna Mohanty and Jennifer Hicks and Sean F. Carroll and Bo Zhou and Hongsheng Zeng and Fan Wang and Rongzhong Lian and Hao Tian and Wojciech Jaśkowski and Garrett Andersen and Odd Rune Lykkebø and Nihat Engin Toklu and Pranav Shyam and Rupesh Kumar Srivastava and Sergey Kolesnikov and Oleksii Hrinchuk and Anton Pechenko and Mattias Ljungström and Zhen Wang and Xu Hu and Zehong Hu and Minghui Qiu and Jun Huang and Aleksei Shpilman and Ivan Sosin and Oleg Svidchenko and Aleksandra Malysheva and Daniel Kudenko and Lance Rane and Aditya Bhatt and Zhengfei Wang and Penghui Qi and Zeyang Yu and Peng Peng and Quan Yuan and Wenxin Li and Yunsheng Tian and Ruihan Yang and Pingchuan Ma and Shauharda Khadka and Somdeb Majumdar and Zach Dwiel and Yinyin Liu and Evren Tumer and Jeremy Watson and Marcel Salathé and Sergey Levine and Scott Delp},
journal= {arXiv preprint arXiv:1902.02441},
year = {2019}
}