There is an increasing interest in machine learning and statistics within the robotics community. At the same time, there has been a growth in the learning community in using robots as motivating applications for new algorithms and formalisms. Considerable evidence of this exists in the use of learning 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 learning papers accepted to IROS and ICRA, and the corresponding number of learning sessions.

The primary vision of our technical committee is as a focus for widely distributing technically rigorous results in shared areas of interest. Without being exclusive, such areas of research interest include

  • learning models of robots, task or environments
  • learning deep hierarchies or levels of representations from sensor & 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


We plan to establish a regular Robot Learning workshop at one of ICRA, IROS or R:SS every year. Ideally, the workshops would focus on a different aspect of robot learning each year. Our first Learning Robots TC workshop, "Robotics Challenges for Machine Learning" was held at IROS 2008. Follow the News link for more information.