Special Issue "Human Friendly Robotics"

Applied-SciencesSpecial Issue Information

Dear Colleagues,

The growing demand to automate daily tasks using new robotic technologies is driving the development of human-friendly robots, i.e., safe and dependable machines, operating in the close vicinity to humans or directly interacting with them in a wide range of domains. The technological shift from classical industrial robots restricted in cages to collaborative robots that are interacting closely with humans, is facing major challenges that need to be addressed.

To this end, we organize the 11th International Workshop on Human-Friendly Robotics (HFR 2018) on 13–14 November, 2018, in Shenzhen, China. The objective of the workshop is to create a focused single-track workshop and to bring together researchers for sharing knowledge in design, control, safety and ethical issues, concerning the application of robots in everyday life. This Special Issue will collect selected papers from the workshop. Potential contributors to this Special Issue are also encouraged to submit and present in the HFR2018 (more information can be found here: HFR2018.org).

List of Topics

Fundamentals of human-friendly robots: mechatronic design, control, analysis (safety measurements, performance metrics, impedance estimation, etc.)

Applications of human-friendly robots: physical interaction (medical and rehabilitation devices, prostheses and orthoses, telerobotics, etc.), cognitive interaction (robot assisted therapy, personal and entertainment robots, robot learning, etc.),

Social and ethical issues

Dr. Fei Chen

Dr. Yan Wu

Dr. Alessandro Di Nuovo

Dr. Dimitri Ognibene

Guest Editors

Manuscript Submission Information

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Guest Editor 

Dr. Fei Chen

Department of Advanced Robotics, Italian Institute of Technology. Via Morego 30, 16163, Genoa, Italy


Dr. Yan Wu

A*STAR Institute for Infocomm Research, 1 Fusionopolis Way #21-01 Connexis (South Tower) 138632, Singapore


Dr. Alessandro Di Nuovo

Sheffield Robotics & Department of Computing, Sheffield Hallam University, Sheffield, S1 1WB, UK


Dr. Dimitri Ognibene

Department of Computer Science and Electronic Engineering, University of Essex, ColchesterCO4 3SQ, UK


Special Issue "Semantic Representations for Behavior Analysis in Robotic system"

Vision sensors and smart cameras have been increasingly used in a variety of applications, such as surveillance, manufacturing, entertainment, robotics, etc. Robotic vision plays a significant role in the era of artificial intelligence.

Among the related topics, motion and behavior analysis in robotic systems have witnessed tremendous progress in the last twenty years. Recently, researchers in robotic vision have shifted their attention from the monitoring of a single subject’ behaviors in a relatively simple environment to that of the behavior of multiple subjects in a crowd environment. In contrast to single subject behavior, multiple behavior analysis faces more challenges, such as complex interactions, diverse semantics and various expressions. This is due to the gap between the information directly extracted from videos and semantic interpretations by human beings.

To bridge this gap, a number of feature representation approaches (e.g., Cuboids, HOG/HOF, HOG3D, and eSURF) have been subsequently reported to address the coherence between the extracted features and the semantic interpretations. Unfortunately, due to redundancy and complexity, these hard-crafted features may lead to diverse variations in semantic representations for social behavior analysis.

In recent years, deep semantic representations have proven to be an effective tool for complicated behavior analysis. Such high-level semantic representations achieve desired performance even if in crowded environments. In addition, statistical approaches, syntactic approaches, and description-based approaches also gain increasing attention in computer vision community.

On the other hand, with the continuous deepening of space exploration, the research and development of space robot systems is becoming more and more important. By analyzing the space information obtained from a sensor system and visual system carried by the space robot, people can have more knowledge and judgments regarding space. Therefore, how to process the information obtained by space robots and how to transmit it to ground stations is also a very important question.

The primary purpose of this Special Issue is to organize a collection of recently-developed semantic representations for behavior analysis in complex environments, spreading over object detection, tracking, motion trajectory acquisition and analysis, semantic feature extraction, social behavior analysis and applications. This Special Issue is intended to be an international forum for researchers to report recent developments in this field. Topics include, but are not limited to:

•  All aspects of robotic vision and UAVs

•  Real-time moving object detection and tracking in crowed environments

•  3D scene reconstruction and occlusion handling

•  Long-term trajectory clustering and analysis for crowed behaviors

•  Probabilistic statistical models for local semantic representation

•  Context model for global semantic representation

•  Event recognition in crowed environments

•  Abnormal behavior detection in crowed environments

•  Real-time algorithms for large scale social behavior analysis

•  Signal processing and communication system of space robot to ground station

•  Deep learning for resource-constrained embedded vision sensor applications

Guest Editor 

Dr. Baochang Zhang 

Beihang University, Beijing, China


Dr. Jungong Han 

Northumbria University, UK


Dr. Chen Chen 

University of North Carolina at Charlotte, USA