Risk Averse Bayesian Reward Learning for Autonomous Navigation from Human Demonstration
Christian Ellis,Maggie Wigness,John Rogers,Craig Lennon,Lance Fiondella,Christian Ellis,Maggie Wigness,John Rogers,Craig Lennon,Lance Fiondella
Traditional imitation learning provides a set of methods and algorithms to learn a reward function or policy from expert demonstrations. Learning from demonstration has been shown to be advantageous for navigation tasks as it allows for machine learning non-experts to quickly provide information needed to learn complex traversal behaviors. However, a minimal set of demonstrations is unlikely to ca...
