Special Issue "Machine Learning Techniques for Assistive Robotics"
Deadline for manuscript submissions: closed (15 March 2020).
Interests: high performance computer vision, GPU programming, 3D data processing (point cloud processing, stereo vision, etcetera) and artificial neural networks
Interests: object detection and action recognition
Assistive robots are a category of robots, which share their area of work and interact with humans. Their main objective is to help humans, especially people with disabilities. To achieve this goal, it is necessary that these robots possess a series of characteristics: the ability to perceive their environment from their sensors and act consequently, to interact with people in a multimodal manner, and to navigate and make decisions autonomously. This complexity demands computationally expensive algorithms to be performed in real-time. So, with the advent of high-end embedded processors, several algorithms could be processed concurrently and in real-time.
All these capabilities involve, to a greater or lesser extent, the use of machine learning techniques. New deep learning techniques have enabled a very important qualitative leap in different areas of perception.
Novel theoretical approaches or practical applications of all aspects involving assistive robotics are welcomed. Reviews, datasets, benchmarks, and surveys of the state-of-the-art are also welcomed. Topics of interest to this Special Issue include, but are not limited to, the following topics:
- Emotion recognition models and systems
- Object recognition & pose estimation for assistive robotics
- Activity recognition
- Navigation, localization, and mapping
- Ambient assistive living
- Robot vision
- Applications for people with disabilities
- Scene understanding & description
- Human-robot interaction
- Embedded systems for assistive robotics
Prof. Dr. Miguel Cazorla
Dr. Sergio Orts-Escolano
Dr. Ester Martinez-Martin
Manuscript Submission Information
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