Deep learning-based human motion recognition for predictive context-aware human-robot collaboration
0 摘要 智能制造领域中，提高人机协作（HRC）的运营效率和安全性的关键是人类操作员和机器人之间实时的情境感知（context awareness）。通过对人类操作员的行为分析，可以提高情境感知的准确性和可靠性。为实现这一目标，本文利用深度学习技术，进行连续人体行为分析和HRC需求预测，从而改进机器人计划和控制，共同完成任务。 我们提出的方法运动到发动机装配的案例中，验证了可行性。 1 引
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相关文章
- 1. Deep Visual Foresight for Planning Robot Motion
- 2. 论文：3D Convolutional Neural Networks for Human Action Recognition
- 3. CVPR 2017 Deep representation learning for human motion prediction and classification 论文解读
- 4. Self-Attention Network for Skeleton-based Human Action Recognition
- 5. Deep learning for sensor-based human activity recognition: overview, challenges and opportunities
- 6. 论文笔记：Deep Convolutional Neural Network On Multichannel Time Series For Human Activity Recognition
- 7. 论文学习：Deep Convolutional Neural Networks On Multichannel Time Series For Human Activity Recognition
- 8. Deep High-Resolution Representation Learning for Human Pose Estimation
- 9. Deep Covariance Descriptors for Facial Expression Recognition
- 10. ResNet--Deep Residual Learning for Image Recognition