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 method, 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. Intention-net maps images from a single monocular camera and given “intentions” directly to robot control. 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.
W. Gao, D. Hsu, W. Lee, S. Shen, and K. Subramanian. Intention-Net: Integrating planning and deep learning for goal-directed autonomous navigation. In S. Levine and V. V. and K. Goldberg, editors, Conference on Robot Learning, volume 78 of Proc. Machine Learning Research, pages 185–194. 2017.
We study the problem of visual navigation in new environments. Humans can easily navigate in an arbitrary environment with crude floor plan and perfect local collision-free navigation. We try to mimic the human navigation in a principled way by integrating high-level planning in the crude global map, i.e. the floor plan and low-level neural-network motion controller. We design the “intention” as the interface to communicate with high-level path planning and local-level neural-network motion controller.
We mainly design two kinds of intention parsers from high-level path planning.
Discretized local move(DLM) intention: We assume that in most cases discretized navigation instructions would be enough for navigation. For example, turning left at the next junction would be enough for human drivers to drive in the real world. We mimic the same procedure by introducing four discretized intentions.
Local path and environment(LPE) intention: The DLM intention is too ad-hoc and relies on pre-defined parameters. To alleviate the issue, we design a map-based intention encoded all the navigation information from the high-level planner.