How can a delivery robot navigate reliably to a destination in a new office building, with minimal prior information? To tackle this challenge, this paper introduces a two-level hierarchical approach, which integrates model-free deep learning and model-based path planning. At the low level, a neural-network motion controller, called the intention-net, is trained end-to-end to provide robust local navigation. The intention-net maps images from a single monocular camera and "intentions" directly to robot controls. At the high level, a path planner uses a crude map, e.g., a 2-D floor plan, to compute a path from the robot's current location to the goal. The planned path provides intentions to the intention-net. Preliminary experiments suggest that the learned motion controller is robust against perceptual uncertainty and by integrating with a path planner, it generalizes effectively to new environments and goals.
Hierarchies are crucial for coping with computational complexity, both in learning and in planning. Combining learning and planning in the same hierarchy is, however, less common. Kaelbling al proposed to learn composable models of parameterized skills with pre- and post-conditions so that a high-level planner can compose these skills to complete complex tasks @cite_15 . Our hierarchical method shares similar thinking, but specializes to navigation tasks. It uses intention instead of general pre- and post-conditions to bind together hierarchical layers and achieves great efficiency.
@cite_15
2736337778
There has been a great deal of work on learning new robot skills, but very little consideration of how these newly acquired skills can be integrated into an overall intelligent system. A key aspect of such a system is compositionality: newly learned abilities have to be characterized in a form that will allow them to be flexibly combined with existing abilities, affording a (good!) combinatorial explosion in the robot's abilities. In this paper, we focus on learning models of the preconditions and effects of new parameterized skills, in a form that allows those actions to be combined with existing abilities by a generative planning and execution system.