Abstract: This letter presents a novel method for environmental exploration that takes safety into account in unknown areas by using recursive Gaussian process regression (RGPR). Safety in unknown ...
Abstract: This article presents a novel model-free distributionally robust framework for a challenging equilibrium-seeking problem (ESP) under fully unknown coupled dynamics. We consider a scenario in ...
Japanese researchers develop an adaptive robot motion system that enables human-like grasping using minimal training data.
Despite rapid robotic automation advancements, most systems struggle to adapt their pre-trained movements to dynamic ...
Researchers develop an adaptive motion system that allows robots to generate human-like movements with minimal data ...
Earth system box models are essential tools for reconstructing long-term climatic and environmental evolution and uncovering ...
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New AI-based technology offers real-time electric vehicle state estimation for safer driving
A research team led by Professor Kanghyun Nam from the Department of Robotics and Mechanical Engineering at DGIST has developed a physical AI-based vehicle state estimation technology that accurately ...
This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...
The published wheels are currently not built with LAMMPS. Thus, running multiscale simulations with molecular dynamics is not possible with this quick installation. For the full functionality it is ...
This uses a Gaussian Processes regression, implemented in R, using the kernlab package. The software takes raw T1-weighted MRI scans, then uses SPM12 for segmentation and normalisation. A slightly ...
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