Machine Learning for Autonomous Skill Acquisition in Robotics

Creating autonomous robots that can assist humans in situations of daily life is a great challenge for machine learning. While this aim has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences, we have yet to achieve the first step of creating robots that can accomplish a multitude of different tasks, triggered by environmental context or higher level instruction. Despite the wide range of machine learning problems encountered during the automatic acquisition of new abilities for robots, we have yet to fulfill the promise that modern learning approaches offer in this context.

Creating complex learning systems that endow robots with the ability to autonomously learn new skills serves both as source of new ideas and benchmark for machine learning approaches. It may among the most promising ways to take robots out of the research labs and bring them into real-world environments.

Specific objectives are:

  • Bring robot learning problems to the attention of the machine learning community!
  • Efficient, scalable skill learning approaches based on model learning, imitation learning, apprenticeship learning, reinforcement learning.
  • Complex motor learning tasks requiring hierarchical structuring.
  • Application in high-dimensional, anthropomorphic robotics in real-time.

Planned events include:

  • An IROS 2011 joint workshop with the IEEE TC on Robot Learning
  • A PASCAL2 Robot Skill Learning Challenge in 2011 (tentative)
  • A NIPS 2011 Workshop together with the CompLACS EU project team

  

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