Content
The term Robot Learning generally denotes the use of learning methods in the context of robotics, which is ubiquitous in modern robotics research. This course aims to provide a systematic introduction to the field, in particular to the various contexts and problem setting where machine learning can be applied and the specific learning methods themselves. This includes topics such as:
• System identification, model learning, residual model learning
• Imitation learning, behavior cloning, learning from demonstration
• Reinforcement Learning (RL), skill learning, offline RL
• Constraint learning, grasp learning, iterative learning control
• Learning to predict plans, learning to warmstart MPC or optimization
• Inverse RL
• Learning as optimization, in-situ learning/trial-and-error vs. offline optimization
• Evolutionary learning
• Online/lifelong learning
• Safe Learning
• Multi-robot learning
