A survey on theories and applications for self-driving cars based on deep learning methods
J Ni, Y Chen, Y Chen, J Zhu, D Ali, W Cao - Applied Sciences, 2020 - mdpi.com
Self-driving cars are a hot research topic in science and technology, which has a great
influence on social and economic development. Deep learning is one of the current key …
influence on social and economic development. Deep learning is one of the current key …
Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning
Transferring human stiffness regulation strategies to robots enables them to effectively and
efficiently acquire adaptive impedance control policies to deal with uncertainties during the …
efficiently acquire adaptive impedance control policies to deal with uncertainties during the …
Deep reinforcement learning for robotic pushing and picking in cluttered environment
In this paper, a novel robotic grasping system is established to automatically pick up objects
in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is …
in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is …
Learning physical human–robot interaction with coupled cooperative primitives for a lower exoskeleton
Human-powered lower exoskeletons have received considerable interests from both
academia and industry over the past decades, and encountered increasing applications in …
academia and industry over the past decades, and encountered increasing applications in …
Remaining useful life predictions for turbofan engine degradation based on concurrent semi-supervised model
As a crucial and expensive component of the aircraft, it is important to effectively predict its
remaining useful life (RUL) so as to reduce maintenance costs and improve maintenance …
remaining useful life (RUL) so as to reduce maintenance costs and improve maintenance …
Salientdso: Bringing attention to direct sparse odometry
Although cluttered indoor scenes have a lot of useful high-level semantic information which
can be used for mapping and localization, most visual odometry (VO) algorithms rely on the …
can be used for mapping and localization, most visual odometry (VO) algorithms rely on the …
Reinforcement learning based data fusion method for multi-sensors
T Zhou, M Chen, J Zou - IEEE/CAA Journal of Automatica …, 2020 - ieeexplore.ieee.org
In order to improve detection system robustness and reliability, multi-sensors fusion is used
in modern air combat. In this paper, a data fusion method based on reinforcement learning is …
in modern air combat. In this paper, a data fusion method based on reinforcement learning is …
Radar and vision fusion for the real-time obstacle detection and identification
X Zhang, M Zhou, P Qiu, Y Huang, J Li - Industrial Robot: the …, 2019 - emerald.com
Purpose The purpose of this paper is the presentation and research of a novel sensor fusion-
based system for obstacle detection and identification, which uses the millimeter-wave radar …
based system for obstacle detection and identification, which uses the millimeter-wave radar …
Embodied tactile perception and learning
Various living creatures exhibit embodiment intelligence, which is reflected by a
collaborative interaction of the brain, body, and environment. The actual behavior of …
collaborative interaction of the brain, body, and environment. The actual behavior of …
Active perception for foreground segmentation: An RGB-D data-based background modeling method
Foreground moving object segmentation is a fundamental problem in many computer vision
applications. As a solution for foreground segmentation, background modeling has been …
applications. As a solution for foreground segmentation, background modeling has been …