Traditional robot planning relies on human-crafted logic representations, but this paper introduces a method to autonomously learn abstract representations from raw robot data. Results show these learned models enable scalable planning for complex tasks without human intervention.
In this paper, we propose unified framework based on deep learning that learns sound abstractiosn for complex robot planning problems and uses it to efficiently perform hierarchical planning.
In this paper, we provide and efficient abstraction based methods to compute task and motion policies for complex robotics task for stochastic environments.