• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of intelligent robotics and applications >Gaze and motion information fusion for human intention inference
【24h】

Gaze and motion information fusion for human intention inference

机译：目光和运动为人类信息融合意图推断

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

An algorithm, named gaze-based multiple model intention estimator (G-MMIE), is presented for early prediction of the goal location (intention) of human reaching actions. The trajectories of the arm motion for reaching tasks are modeled by using an autonomous dynamical system with contracting behavior towards the goal location. To represent the dynamics of human arm reaching motion, a neural network (NN) is used. The parameters of the NN are learned under constraints derived based on contraction analysis. The constraints ensure that the trajectories of the dynamical system converge to a single equilibrium point. In order to use the motion model learned from a few demonstrations in new scenarios with multiple candidate goal locations, an interacting multiple-model (IMM) framework is used. For a given reaching motion, multiple models are obtained by translating the equilibrium point of the contracting system to different known candidate locations. Hence, each model corresponds to the reaching motion that ends at the respective candidate location. Further, since humans tend to look toward the location they are reaching for, prior probabilities of the goal locations are calculated based on the information about the human's gaze. The posterior probabilities of the models are calculated through interacting model matched filtering. The candidate location with the highest posterior probability is chosen to be the estimate of the true goal location. Detailed quantitative evaluations of the G-MMIE algorithm on two different datasets involving 15 subjects, and comparisons with state-of-the-art intention inference algorithms are presented.
机译：一个名叫gaze-based多个模型的算法目的估计量(G-MMIE),提出了早期预测的目标位置(意图)人类的行为。达到任务的手臂运动建模使用一个自治动力系统承包行为向目标位置。代表人类手臂的动力学达到运动,使用神经网络(NN)。参数的神经网络学习下基于收缩约束派生分析。动力系统收敛于的轨迹一个平衡点。从几个示威运动模型新场景与多个候选目标地点,一个交互多模(IMM)使用框架。多个模型是通过翻译的承包系统的平衡点不同候选人的位置。模型对应的运动在各自的候选位置结束。进一步,因为人们倾向于朝位置他们到达之前概率的目标位置基于信息计算人类的目光。通过交互模型计算模型匹配滤波。后验概率最高的选择真正的估计目标位置。G-MMIE算法的定量评估在两个不同的数据集涉及15个主题,和比较先进的意图推理算法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号