3 accepted papers
Learning-based policies have demonstrated success in many robotic applications, but often lack explainability. We propose a neuro-symbolic explanation framework that generates a weighted signal temporal logic (wSTL) specification which describes a robot policy in a human-interpretable form. Existing
We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonom
Deep Reinforcement Learning has been used to exploit specific environments, but has difficulty transferring learned policies to new situations. This issue poses a problem for practical applications of Reinforcement Learning, as real-world scenarios may introduce unexpected differences that drastical…