Call for papers
CFP: IROS Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics 2016 (ML-HLCR 2016)
[CALL FOR PAPERS]
Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics 2016
(ML-HLCR 2016) at IEEE/RSJ IROS
(Full-day workshop)
October 14th 2016
Daejeon, Korea.
http://mlhlcr2016.tanichu.com/
*****DEADLINE IS EXTENDED TO August 22, 2016 *******
We kindly invite you to submit your contributions to this workshop. For the detailed information, please visit our website.
*Abstract of the workshop
Integrating multi-level sensory-motor and cognitive capabilities is essential for developing robotic systems that can adaptively act in our daily environment in active collaboration with humans. In this workshop, we aim to share knowledge about the state-of-the-art machine learning methods that contribute to modeling sensory-motor and cognitive capabilities in robotics and to exchange views among cutting-edge robotics researchers with a special emphasis on adaptive high-level cognition.
Our daily environment is full of uncertainties with complex objects and challenging tasks. A robot is not only required to deal with things appropriately in a physical manner but also required to perform logical and/or linguistic tasks in the real world. Conventionally, symbol-based and/or rule-based approaches have been employed to model high-level cognitive capabilities in robotics. However, it has been pointed out that such conventional methods could not deal with the uncertainty that is inevitably found in the physical environment and natural human-robot communication.
Recent advances in machine learning techniques, including deep learning and hierarchical Bayesian modeling, are providing us with new possibilities to integrate high-level and low-level cognitive capabilities in robotics. It became clear that such learning methods are indispensable to create robots that can effectively deal with uncertainty while acting smart in the real world.
In this workshop, we will investigate how to create synergies so that advanced learning of sensorimotor and cognitive capabilities can interact to create a bootstrapping effect in different levels of skill acquisition.
*Topics of interest
Multimodal machine learning for robotics
Deep learning for robotics
Computational approaches to the study of development and learning
Bayesian modeling for high-level cognitive capabilities
Emergence of communication
Segmentation of time-series information
Probabilistic programming and reasoning
Language acquisition
Symbol grounding
Human-robot communication and collaboration based on machine learning
Human-assisted learning
Imitation learning and Skill acquisition
Cognitive and perceptual development
Exploration and learning in animals and robots
Social and emotional learning in humans and robots
Curiosity and intrinsic motivation
Affordance learning
The topics of the contributed papers are not limited to the topics shown above.
*Call for contributions
Participants are required to submit a contribution as:
- Extended abstract (maximum 2 pages in length)
All submissions will be reviewed on the basis of relevance, novelty, originality, significance, soundness and clarity. At least two referees will review each submission independently.
Accepted papers will be presented during the workshop in a poster session.
A small number of selected papers will be presented as oral presentations or spotlight talks.
*Submission
Submissions must be in PDF following the IEEE conference style in two-columns.
http://ras.papercept.net/conferences/support/support.php
Send your PDF manuscript indicating [ML-HLCR 2016] in the subject to the following emai:
mlhlcr2016[at]em.ci.ritsumei.ac.jp
*Important dates*
August 22, 2016 (EXTENDED)- Contributions submission deadline
August 31, 2016 - Notification of acceptance
October 14, 2016 - Workshop
*Invited speakers
Jun Tani, KAIST
Komei Sugiura, NICT
Xavier Hinaut, INRIA
Justus Piater, University of Innsbruck
Tadahiro Taniguchi, Ritsumeikan University
Kuniaki Noda, Nissan North America
*Organizers
Takayuki Nagai, The University of Electro-Communications
Tetsuya Ogata, Waseda University
Emre Ugur, Bogazici University
Yiannis Demiris, Imperial College London
Tadahiro Taniguchi, Ritsumeikan University, Japan,
See more details in: