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.
This paper proposes a new method for robots to plan actions in complex environments, even when the environment is unknown. The robot learns to create its own high-level actions without needing pre-programmed ones. This allows the robot to quickly solve new problems in unseen environments. The method is shown to be faster and achieve significantly better solutions than existing approaches
In this paper, we use deep learning to identify critical regions and automatically construct hierarchical state and action abstractions. We use these hierarchical abstractions with a multi-source mutli-directional hierarchical planner to compute solutions for robot planning problem.
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.