机器人
计算机科学
参数化复杂度
人机交互
运动(物理)
适应(眼睛)
人工智能
预测建模
鉴定(生物学)
机器学习
人在回路中
人体运动
算法
物理
光学
生物
植物
作者
Weiye Zhao,Liting Sun,Changliu Liu,Masayoshi Tomizuka
标识
DOI:10.23919/acc45564.2020.9147277
摘要
Human motion prediction is non-trivial in modern industrial settings. Accurate prediction of human motion can not only improve efficiency in human-robot collaboration, but also enhance human safety in close proximity to robots. Although many prediction models have been proposed with various parameterization and identification approaches, some fundamental questions remain unclear: what is the necessary parameterization of a prediction model? Is online adaptation of models necessary? Can a prediction model help improve safety and efficiency during human-robot collaboration? These unaddressed questions result from the difficulty of quantitatively evaluating different prediction models in a closed-loop fashion in real human-robot interaction. This paper develops a method to evaluate the closed-loop performance of different prediction models. In particular, we compare models with different parameterizations and models with or without online parameter adaptation. Extensive experiments were conducted on a human-robot collaboration platform. The experimental results demonstrate that human motion prediction significantly enhance the collaboration efficiency and human safety. Adaptable prediction models that are parameterized by neural networks achieve better performance.
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