Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration
Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration
复制标题
人机协作的适应性人类意图和轨迹预测
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Changliu Liu
中科院分区：
文献类型：
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作者：
Abulikemu Abuduweili;Siyan Li;Changliu Liu
为了实现安全高效的人机协作，生成对人类行为的高保真预测至关重要。做出准确预测的挑战在于人类行为的随机性和异质性。本文介绍了一种通过多任务模型来预测人的轨迹和意图的方法，该模型适用于不同的人类主体。提出了一种非线性递归最小二乘参数自适应算法（NRLS-PAA）实现在线自适应。实验结果验证了该方法的有效性和灵活性。特别是，在线自适应可以将新的人类受试者的轨迹预测误差减少28%以上。所提出的人类预测方法具有高度的灵活性、数据效率和通用性，可以支持快速集成HRC系统以完成用户指定的任务。
To engender safe and efficient human-robot collaboration, it is critical to generate high-fidelity predictions of human behavior. The challenges in making accurate predictions lie in the stochasticity and heterogeneity in human behaviors. This paper introduces a method for human trajectory and intention prediction through a multi-task model that is adaptable across different human subjects. We develop a nonlinear recursive least square parameter adaptation algorithm (NRLS-PAA) to achieve online adaptation. The effectiveness and flexibility of the proposed method has been validated in experiments. In particular, online adaptation can reduce the trajectory prediction error by more than 28% for a new human subject. The proposed human prediction method has high flexibility, data efficiency, and generalizability, which can support fast integration of HRC systems for user-specified tasks.