Reducing Conservativeness in Safety Guarantees by Learning Disturbances Online: Iterated Guaranteed Safe Online Learning

@inproceedings{Gillula2012ReducingCI,
  title={Reducing Conservativeness in Safety Guarantees by Learning Disturbances Online: Iterated Guaranteed Safe Online Learning},
  author={Jeremy H. Gillula and Claire J. Tomlin},
  booktitle={Robotics: Science and Systems},
  year={2012}
}
Reinforcement learning has proven itself to be a powerful technique in robotics, however it has not often been employed to learn a controller in a hardware-in-the-loop environment due to the fact that spurious training data could cause a robot to take an unsafe (and potentially catastrophic) action. One approach to overcoming this limitation is known as Guaranteed Safe Online Learning via Reachability (GSOLR), in which the controller being learned is wrapped inside another controller based on… CONTINUE READING
Highly Cited
This paper has 20 citations. REVIEW CITATIONS