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IEEE Technical Committee on Robot Learning

The robot hardware is progressively becoming more complex, which leads to growing interest in applying machine learning and statistics approaches within the robotics community. At the same time, there has been a growth within the machine learning community in using robots as motivating applications for new algorithms and formalisms. Considerable evidence of this exists in the use of robot learning approaches in high-profile competitions such as RoboCup and the DARPA Challenges, and the growing number of research programs funded by governments around the world. Additionally, the volume of research is increasing, as shown by the number of robot learning papers accepted to IROS and ICRA, and the corresponding number of learning sessions.


iCub robot archer - additional photo - IMG 5144  Pancake flipping robot - IMG 4086
The primary goal of the Technical Committee on Robot Learning is to act as a focus point for wide distribution of technically rigorous results in the shared areas of interest around robot learning. Without being exclusive, such areas of research interest include:

  • learning models of robots, tasks or environments
  • learning deep hierarchies or levels of representations, from sensor and motor representations to task abstractions
  • learning of plans and control policies by imitation and reinforcement learning
  • integrating learning with control architectures
  • methods for probabilistic inference from multi-modal sensory information (e.g., proprioceptive, tactile, vison)
  • structured spatio-temporal representations designed for robot learning such as low-dimensional embedding of movements
  • developmental robotics and evolutionary-based learning approaches


COMAN robot learning to walk - MVI 4773COMAN robot learning to walk - IMG 4682  HOAP-2 holding eraserHOAP-2 kinesthetic teaching with force sensor 01

News

  • [May 21, 2013] New Job opening – Post-doc in Robot Learning on topic: “Machine Learning for Robotics”. Details here: http://kormushev.com/news/postdoc-opening-in-machine-learning-for-robotics-2013/
  • [August 10, 2012] New IROS 2012 Workshop: “Beyond Robot Grasping – Modern Approaches for Dynamic Manipulation”. The workshop will be held on October 12, 2012 in Algarve, Portugal. More information at the website of the workshop: http://www.ias.informatik.tu-darmstadt.de/Research/IROS2012
  • [March 28, 2012] New AIMSA 2012 Workshop organized by the TC on “Advances in Robot Learning and Human-Robot Interaction”. The workshop will be held on September 12, 2012 in Varna, Bulgaria. More information at the website of the workshop: http://kormushev.com/AIMSA-2012/
  • [March 13, 2012] New chairs of the TC. After three very successful years for this TC on Robot Learning, the founding chairs Jan Peters, Jun Morimoto, Russ Tedrake and Nicholas Roy are stepping down as chairs of the committee. They will be replaced by Petar Kormushev, Edwin Olson, Ashutosh Saxena, and Wataru Takano who have kindly agreed to take the reign of the committee. Please see the changes in the mailing list addresses here.

Recent Activities of the Technical Committee

The technical committee regularly organizes special sessions associated with the “Robot learning” RAS keyword. If you want your paper to be considered for such a session and have used the above keyword in your submission, please forward an email to the TC co-chairs (contact info at: http://www.ieee-ras.org/robot-learning/contact). The technical committee will not be involved in the reviewing process but will organize the session based on the list of accepted submissions with this keyword.


Ironing robot - IMG 4366WAM robot - ArmInFridge

TC-organized Workshops

This is a summary of the workshops which were organized by the IEEE TC on Robot Learning:

Chairs of the Technical Committee

ashutosh saxena
Ashutosh Saxena
Cornell University, USA

edwin olson
Edwin Olson
University of Michigan, USA

petar kormushev
Petar Kormushev
Italian Institute of Technology, Italy

wataru takano
Wataru Takano
University of Tokyo, Japan

 

Technical Committee Website:

http://www.learning-robots.de