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