Recently, brain-inspired learning and deep learning have obtained wide attention in the areas of autonomous vehicles and robots. The goal is that the robot and vehicle can learn/adapt its surrounding environment to conduct different tasks via intelligent learning approaches.
Among many intelligent learning approaches, deep learning has gained a series of success across various areas including image, lidar, decision-making as well as user-interaction data. However, there are still many intriguing research problems, such as accuracy and robustness under uncertainties, learning efficiency for real robotic environment. An alternative has been proposed in the form of brain-inspired learning approaches such as spiking neuron network, which is a major research topic in theoretical neuroscience and neuromorphic engineering. Compared to deep learning approach, brain-inspired learning approaches exploit data-driven updates to gain efficiency by reducing redundant information based on asynchronous event processing.
This Research Topic aims to bring together research work in new advances of brain-inspired learning and deep learning, with an emphasis on the intersection of deep learning and bio-inspired learning based approaches, and learning approaches combining with sensing data from neuromorphic sensors. It will feature original research papers related to (but not limited to) learning theory, feature representation, and end-to-end automatic systems for intelligent perception and decision-making. The survey /review papers are also welcome. The topics of interest include, but not limited to:
- Novel learning method for intelligent perception
- Multi-modal/task learning for decision-making
- Reinforcement deep learning and bio-inspired learning
- Adversarial deep learning
- Online learning via deep network and spiking neuron network
- End-to-end learning system for sensing and control
- Novel robotics application and benchmarks with neuromorphic sensors
- Autonomous robotics with neuromorphic sensors
- Visual simultaneous localization and mapping (V-SLAM) via deep learning
- Autonomous robotics with deep learning and bio-inspired learning
- Deep spiking neuron network
Recently, brain-inspired learning and deep learning have obtained wide attention in the areas of autonomous vehicles and robots. The goal is that the robot and vehicle can learn/adapt its surrounding environment to conduct different tasks via intelligent learning approaches.
Among many intelligent learning approaches, deep learning has gained a series of success across various areas including image, lidar, decision-making as well as user-interaction data. However, there are still many intriguing research problems, such as accuracy and robustness under uncertainties, learning efficiency for real robotic environment. An alternative has been proposed in the form of brain-inspired learning approaches such as spiking neuron network, which is a major research topic in theoretical neuroscience and neuromorphic engineering. Compared to deep learning approach, brain-inspired learning approaches exploit data-driven updates to gain efficiency by reducing redundant information based on asynchronous event processing.
This Research Topic aims to bring together research work in new advances of brain-inspired learning and deep learning, with an emphasis on the intersection of deep learning and bio-inspired learning based approaches, and learning approaches combining with sensing data from neuromorphic sensors. It will feature original research papers related to (but not limited to) learning theory, feature representation, and end-to-end automatic systems for intelligent perception and decision-making. The survey /review papers are also welcome. The topics of interest include, but not limited to:
- Novel learning method for intelligent perception
- Multi-modal/task learning for decision-making
- Reinforcement deep learning and bio-inspired learning
- Adversarial deep learning
- Online learning via deep network and spiking neuron network
- End-to-end learning system for sensing and control
- Novel robotics application and benchmarks with neuromorphic sensors
- Autonomous robotics with neuromorphic sensors
- Visual simultaneous localization and mapping (V-SLAM) via deep learning
- Autonomous robotics with deep learning and bio-inspired learning
- Deep spiking neuron network
