AIMC Topic: Motion

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Efficient multitask learning with an embodied predictive model for door opening and entry with whole-body control.

Science robotics
Robots need robust models to effectively perform tasks that humans do on a daily basis. These models often require substantial developmental costs to maintain because they need to be adjusted and adapted over time. Deep reinforcement learning is a po...

Evaluation of text-to-gesture generation model using convolutional neural network.

Neural networks : the official journal of the International Neural Network Society
Conversational gestures have a crucial role in realizing natural interactions with virtual agents and robots. Data-driven approaches, such as deep learning and machine learning, are promising in constructing the gesture generation model, which automa...

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN.

Sensors (Basel, Switzerland)
The defocus or motion effect in images is one of the main reasons for the blurry regions in digital images. It can affect the image artifacts up to some extent. However, there is a need for automatic defocus segmentation to separate blurred and sharp...

Vision-Based Learning from Demonstration System for Robot Arms.

Sensors (Basel, Switzerland)
Robotic arms have been widely used in various industries and have the advantages of cost savings, high productivity, and efficiency. Although robotic arms are good at increasing efficiency in repetitive tasks, they still need to be re-programmed and ...

Probing Diffusive Dynamics of Natural Tubule Nanoclays with Machine Learning.

ACS nano
Reproducibility of the experimental results and object of study itself is one of the basic principles in science. But what if the object characterized by technologically important properties is natural and cannot be artificially reproduced one-to-one...

MR-assisted PET respiratory motion correction using deep-learning based short-scan motion fields.

Magnetic resonance in medicine
PURPOSE: We evaluated the impact of PET respiratory motion correction (MoCo) in a phantom and patients. Moreover, we proposed and examined a PET MoCo approach using motion vector fields (MVFs) from a deep-learning reconstructed short MRI scan.

Using Artificial Neuro-Molecular System in Robotic Arm Motion Control-Taking Simulation of Rehabilitation as an Example.

Sensors (Basel, Switzerland)
Under the delicate control of the brain, people can perform graceful movements through the coordination of muscles, bones, ligaments, and joints. If artificial intelligence can be used to establish a control system that simulates the movements of hum...

Recognition of Human Body Feature Changes in Sports Health Based on Deep Learning.

Computational and mathematical methods in medicine
With the rapid development of social economy and the extensive and in-depth development of national fitness activities, national physical fitness monitoring and research work has achieved rapid development. In recent years, the application of deep le...

Image Recognition and Extraction of Students' Human Motion Features Based on Graph Neural Network.

Computational intelligence and neuroscience
In order to improve students' overall subhealth behavior, teenagers' physical health problems have attracted more and more attention. The state clearly requires students to increase the number and frequency of exercise in school. In order to study th...

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks.

Sensors (Basel, Switzerland)
Humans learn movements naturally, but it takes a lot of time and training to achieve expert performance in motor skills. In this review, we show how modern technologies can support people in learning new motor skills. First, we introduce important co...