ArticleFrontiers in neuroscience2022
A brain-inspired intention prediction model and its applications to humanoid robot.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Interpretable EEG Emotion Classification via CNN Model and Gradient-Weighted Class Activation Mapping.Brain sciences · 2025Article
- Dynamic Cascade Spiking Neural Network Supervisory Controller for a Nonplanar Twelve-Rotor UAV.Sensors (Basel, Switzerland) · 2025Article
- Brain-inspired bodily self-perception model for robot rubber hand illusion.Patterns (New York, N.Y.) · 2023Article
- BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation.Patterns (New York, N.Y.) · 2023Article
- Editorial: Neuro-cognition in human movement: from fundamental experiments to bio-inspired innovation.Frontiers in neurologyArticle
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2 authors.
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Abstract
With the development of artificial intelligence and robotic technology in recent years, robots are gradually integrated into human daily life. Most of the human-robot interaction technologies currently applied to home service robots are programmed by the manufacturer first, and then instruct the user to trigger the implementation through voice commands or gesture commands. Although these methods are simple and effective, they lack some flexibility, especially when the programming program is contrary to user habits, which will lead to a significant decline in user experience satisfaction. To make that robots can better serve human beings, adaptable, simple, and flexible human-robot interaction technology is essential. Based on the neural mechanism of reinforcement learning, we propose a brain-inspired intention prediction model to enable the robot to perform actions according to the user's intention. With the spike-timing-dependent plasticity (STDP) mechanisms and the simple feedback of right or wrong, the humanoid robot NAO could successfully predict the user's intentions in Human Intention Prediction Experiment and Trajectory Tracking Experiment. Compared with the traditional Q-learning method, the training times required by the proposed model are reduced by (
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