[robotics-worldwide] [meetings] IROS19 Workshop on

Hamidreza Kasaei h.kasaei2012 at gmail.com
Mon Jul 22 02:51:27 PDT 2019


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Call for paper 
====================== 

Dear Colleagues, 
you are invited to submit your extended abstract and/or paper to Open-Ended
Learning for Object Perception and Grasping workshop at IROS 2019. 

  - IROS 2019 Workshop on “Open-Ended Learning for Object Perception and
Grasping: Current Successes and Future Challenges” 
  
  - Workshop date: 8 November 2019
  - Submission Deadline: 20 September 2019
  - Notification: 1 October 2019
  - URL: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.ai.rug.nl_oel&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=71vw2-CeHdtfE7w5LGRY47qTY04LSp7Xzjp3W_SYRz8&s=1_7gHcQMribxTn7WDOIyt6wt07xCo2qktTxDRtVUQKk&e= 
  - Location: The Venetian Macao, Macau, China

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Workshop focus: 
====================== 

Service robots are expected to be more autonomous and efficiently work in
human-centric environments. For these robots, object perception and grasping
are two challenging tasks due to the high demand for accurate and real-time
response under changing and unpredictable environmental conditions. Although
many problems have already been understood and solved successfully, many
challenges still remain. Open-Ended Learning is one of these challenges
waiting for many improvements. Cognitive science revealed that humans learn
to recognize object categories and grasp affordances ceaselessly over time.
This ability allows adapting to new environments by enhancing their
knowledge from the accumulation of experiences and the conceptualization of
new object categories. Taking this theory as an inspiration, an autonomous
robot must have the ability to process visual information and conduct
learning and recognition tasks in a concurrent and open-ended fashion. In
this context, “open-ended” implies that the set of object categories to be
learned is not known in advance, and the training instances are extracted
from on-line experiences of a robot, and become gradually available over
time, rather than being completely available at the beginning of the
learning process. This way the robot adapts its perception and grasping
skills over time to different environments. This cognitive robotic approach
allows us to better integrate them into our societies.  

Topics of interest include (but not limited to): 

  - Architectures for open-ended learning
  - Transfer learning from one to another type of robot hand
  - Open-ended grasping of deformable objects
  - Lifelong learning and adaptation for autonomous robots
  - Cognitive robotics
  - Deep learning for task-informed grasping
  - Deep transfer learning for object perception
  - Knowledge transfer and avoidance of catastrophic forgetting
  - Affordance learning and task informed grasping
  - Challenges of Human-Robot collaborative manipulation
  - Grasping and object manipulation
  - 3D object category learning and recognition
  - Active perception and scene interpretation
  - Coupling between object perception and manipulation
  - Learning from demonstrations

======================
Submission guidelines: 
====================== 

Submissions are welcome in any of the two categories: 

  - Extended abstract (Maximum 2 pages): new ideas on Task-informed grasping
and/or late-breaking results; 
  - Full paper (maximum 6 pages in): will be accepted based on their
quality, originality, and relevance to the workshop. Submitted papers should
not be under consideration for publication anywhere else. 

Submissions should follow the IEEE format. 
Please submit your paper by emailing it to oel.workshop at gmail.com by the
following deadline. 

======================
Important dates: 
====================== 

  - Submission Deadline: 20 September 2019
  - Notification: 1 October 2019
  - Workshop Date: 8 November 2019

Kind regards, 
Hamidreza Kasaei 
www.ai.rug.nl/hkasaei

on behalf of the organizing committee 
Dr. Hamidreza Kasaei, the University of Groningen, The Netherlands 
Dr. Amir Ghalamzan Esfahani, University of Lincoln, UK 



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