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How can a delivery robot navigate reliably to a destination in a new officebuilding, with minimal prior information? To tackle this challenge, this paperintroduces a two-level hierarchical approach, which integrates model-free deeplearning and model-based path planning. At the low level, a neural-networkmotion controller, called the intention-net, is trained end-to-end to providerobust local navigation. The intention-net maps images from a single monocularcamera and "intentions" directly to robot controls. At the high level, a pathplanner uses a crude map, e.g., a 2-D floor plan, to compute a path from therobot's current location to the goal. The planned path provides intentions tothe intention-net. Preliminary experiments suggest that the learned motioncontroller is robust against perceptual uncertainty and by integrating with apath planner, it generalizes effectively to new environments and goals.

Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation