  In unknown cluttered and dynamic environments such as disaster scenes, mobile
robots need to perform target-driven navigation in order to find people or
objects of interest, while being solely guided by images of the targets. In
this paper, we introduce NavFormer, a novel end-to-end transformer architecture
developed for robot target-driven navigation in unknown and dynamic
environments. NavFormer leverages the strengths of both 1) transformers for
sequential data processing and 2) self-supervised learning (SSL) for visual
representation to reason about spatial layouts and to perform
collision-avoidance in dynamic settings. The architecture uniquely combines
dual-visual encoders consisting of a static encoder for extracting invariant
environment features for spatial reasoning, and a general encoder for dynamic
obstacle avoidance. The primary robot navigation task is decomposed into two
sub-tasks for training: single robot exploration and multi-robot collision
avoidance. We perform cross-task training to enable the transfer of learned
skills to the complex primary navigation task without the need for
task-specific fine-tuning. Simulated experiments demonstrate that NavFormer can
effectively navigate a mobile robot in diverse unknown environments,
outperforming existing state-of-the-art methods in terms of success rate and
success weighted by (normalized inverse) path length. Furthermore, a
comprehensive ablation study is performed to evaluate the impact of the main
design choices of the structure and training of NavFormer, further validating
their effectiveness in the overall system.
