[robotics-worldwide] [journals] Special Issue on Machine learning techniques for assistive robotics

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[robotics-worldwide] [journals] Special Issue on Machine learning techniques for assistive robotics

Miguel Cazorla
Dear colleagues:

Journal Electronics (IF 2.110, Q2) is currently running a Special Issue
entitled "Machine learning techniques for assistive robotics" with Ester
Martinez-Martin, Sergio Orts-Escolano and Miguel Cazorla serving as guest
editors for this issue. Electronics is an open access journal.

The assistive robots are a category of robots, which share their area of
work and interact with humans. Its 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: ability to perceive
its environment from their sensors and act consequently, to interact with
people in a multimodal manner, 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. The new techniques of deep learning have meant
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

Important Dates:
Deadline: 31 July 2019
Papers are published upon acceptance, regardless of the Special Issue
publication date
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