The application of Artificial Intelligence (AI) across a wide range of domains comes with both
high expectations of its benefits and dire predictions of misuse. While AI systems have …

Interactive Imitation Learning in Robotics: A Survey Page 1 Interactive Imitation Learning in
Robotics: A Survey Page 2 Other titles in Foundations and Trends® in Robotics A Survey on …

Reinforcement learning has shown great potential for learning sequential decision-making
tasks. Yet, it is difficult to anticipate all possible real-world scenarios during training, causing …

In order to deploy robots that could be adapted by non-expert users, interactive imitation
learning (IIL) methods must be flexible regarding the interaction preferences of the teacher …

To use reinforcement learning from human feedback (RLHF) in practical applications, it is
crucial to learn reward models from diverse sources of human feedback and to consider …

Interactive Reinforcement Learning (IRL) has shown promising results in decreasing the
learning times of Reinforcement Learning algorithms by incorporating human feedback and …

Curriculum value-based reinforcement learning (RL) solves a complex target task by reusing
action-values across a tailored sequence of related tasks of increasing difficulty. However …

The requirement for a high number of training episodes has been a major limiting factor for
the application of Reinforcement Learning (RL) in robotics. Learning skills directly on real …

Interactive Reinforcement Learning (IRL) uses human input to improve learning speed and
enable learning in more complex environments. Human action advice is here one of the …

One of the key challenges in implementing reinforcement learning methods for real-world
robotic applications is the design of a suitable reward function. In field robotics, the absence …