[robotics-worldwide] [meetings] CFP - IROS 2017 Workshop on Human Movement Understanding for Humanoid and Wearable Robots

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[robotics-worldwide] [meetings] CFP - IROS 2017 Workshop on Human Movement Understanding for Humanoid and Wearable Robots

Katja Mombaur
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                                2nd Call for Posters and Participation
                                         IROS 2017 Workshop on
     Human Movement Understanding for Humanoid and Wearable Robots
                       September 28, 2017, Vancouver, BC, Canada

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Organizers:
Dana Kulic, Universty of Waterloo, Canada, [hidden email]
Katja Mombaur, Heidelberg University, Germany, [hidden email]

Objectives:
The human body and its movement have long been a focus of study in
physiology, anatomy, biomechanics and neuroscience. More recently, the
design and movement strategies of the body have become an important
source of inspiration for humanoid and wearable robotics researchers.
With its unique combination of physical structure and neuromuscular
control mechanisms, the human body can perform a wide range of agile and
dexterous movements, often in the most efficient way possible. Over the
past three decades, much progress has been made in developing methods
for the modeling, analysis, synthesis, and optimization of articulated
bodies. These methods have not only improved our knowledge and
understanding of the mechanisms underlying human movement, but have also
been effectively applied to humanoid robot design and control. More
generally, the use of quantitative human motion analysis and synthesis
is creating new applications in, e.g., medical diagnosis, monitoring and
feedback during rehabilitation and sports training, animation, ergonomic
analysis and design, and improved rehabilitation and assistive robots
and devices. This workshop aims to bring together the main research
themes in this emerging field, to highlight the utility of movement
science for robotics, synthesize the key insights learned to date, and
to illustrate emerging research contributing both to robotics and human
movement understanding.


Topics of Interest:
-  Kinematic and dynamic modeling and analysis of the human body
- Dynamic parameter estimation and optimum experimental design
- Optimal control of human and humanoid motions
-  Inverse optimal control for identification of objective functions
during human motor
control
-  Motion recognition, segmentation, modeling, and analysis
-  Quantitative analysis methods for rehabilitation and sports training
-  Neuromuscular control
-  Musculoskeletal dynamics
-  Rehabilitation robotics
-  Human motion analysis and understanding for imitation learning and
human-robot interaction
-  Human-inspired control algorithms for robots
-  Model-predictive control for movement science
- Human movement informing the design and control of assistive devices,
exoskeletons
and prostheses
- Robotics-based motion synthesis

Confirmed Speakers:
- Tamim Asfour, KIT, Germany
- Jan Babic, Josef Stefan Institute, Slovenia
- Mehdi Benallegue, AIST, Tsukuba, Japan
- Etienne Burdet, Imperial College, London, UK
- Tamar Flash, Weizmann Institute, Israel
- Robert Gregg, University of Texas at Dallas, USA
- Herman van der Kooij, University of Twente / TU Delft, The Netherlands
- Dana Kulic, University of Waterloo, Canada.
- Katja Mombaur, Heidelberg University, Germany
- Gentiane Venure, TUAT, Japan
- Katsu Yamane, Disney Research, USA
- Eiichi Yoshida, AIST Tsukuba, Japan


Poster presentations:
If you want to present a poster at the workshop, please submit your 1-2
page extended abstract (IEEE style, 1-2 figures) until July 15, 2017 to
[hidden email].
Notifications of acceptance will be sent until July 20, 2017.
The abstracts will be published on the workshop webpage.

More information at:
https://urldefense.proofpoint.com/v2/url?u=https-3A__iros2017wshumanmovementunderstanding.wordpress.com_&d=DwICaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=0GSxag9w35hLDtm-tyAGJb0ueTy7yoCOVbTPmGNmstI&s=wCn0JZddePaT1tPw4g-p-562TE_JLCyyKUeUPL3SQLQ&e= 


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