Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation
This addresses the problem of autonomous robot navigation in large, multi-room settings for robotics applications, representing an incremental improvement over existing methods.
The paper tackles robot navigation in complex environments by developing an imitation learning system that uses panoramic goal views and RGBD inputs to navigate without maps or compasses, achieving higher accuracy and shorter paths with fewer training examples and less time compared to baselines.
We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real environment and trains an agent from pre-generated expert trajectories to navigate to any position given a panoramic view of the goal and the current visual input without relying on map, compass, odometry, or relative position of the target at runtime. Our end-to-end trained agent uses RGB and depth (RGBD) information and can handle large environments (up to $1031m^2$) across multiple rooms (up to $40$) and generalizes to unseen targets. We show that when compared to several baselines our method (1) requires fewer training examples and less training time, (2) reaches the goal location with higher accuracy, and (3) produces better solutions with shorter paths for long-range navigation tasks.