Hybrid Recurrent Neural Network Architecture-Based Intention Recognition for Human-Robot Collaboration

Computer Science

• Robot 100%
• Human Operator 100%
• Recurrent Neural Network 100%
• Long Short-Term Memory Network 100%
• Neural Network Architecture 100%
• Activation Function 100%
• Bidirectional Long Short-Term Memory Network 100%
• Human-Robot Interaction 100%
• Hybrid Network 100%
• Experimental Result 50%
• Deep Learning 50%
• Deep Learning 50%
• Prediction Performance 50%
• Network Architecture 50%
• Sliding Window 50%
• Learning Approach 50%
• Network Performance 50%
• Spatiotemporal Data 50%
• Representation Learning 50%
• And Gate 50%
• Hybrid Architecture 50%
• Human Motions 50%
• Motion Capture 50%

Keyphrases

• Architecture-centric 100%
• Neural Network Architecture 100%
• Intention Recognition 100%
• Human-robot Collaboration 100%
• Hybrid Recurrent Neural Network 100%
• Deep Learning Methods 66%
• Human Operator 66%
• Activation Function 66%
• Performance Prediction 33%
• Sliding Window 33%
• Hybrid Network 33%
• Abnormal Data 33%
• Network Performance 33%
• Spatio-temporal Data 33%
• Feature Learning 33%
• Bidirectional Long Short-term Memory (BiLSTM) 33%
• Assembly Task 33%
• Improved LSTM 33%
• LSTM Network 33%
• Temporal Dependency Modeling 33%
• Human Motion Capture 33%
• Amplitude Limiting 33%
• Hybrid Architecture 33%
• Activation Gate 33%
• Collaborative Space 33%
• Robot Motion 33%
• Network Training 33%
• Motion Capture Device 33%
• Motion Prediction 33%
• Hybrid Network Architecture 33%
• Sequential Data 33%
• Collaborative Robots 33%
• Network Testing 33%