  Recently, visual-language navigation (VLN) -- entailing robot agents to
follow navigation instructions -- has shown great advance. However, existing
literature put most emphasis on interpreting instructions into actions, only
delivering "dumb" wayfinding agents. In this article, we devise LANA, a
language-capable navigation agent which is able to not only execute
human-written navigation commands, but also provide route descriptions to
humans. This is achieved by simultaneously learning instruction following and
generation with only one single model. More specifically, two encoders,
respectively for route and language encoding, are built and shared by two
decoders, respectively, for action prediction and instruction generation, so as
to exploit cross-task knowledge and capture task-specific characteristics.
Throughout pretraining and fine-tuning, both instruction following and
generation are set as optimization objectives. We empirically verify that,
compared with recent advanced task-specific solutions, LANA attains better
performances on both instruction following and route description, with nearly
half complexity. In addition, endowed with language generation capability, LANA
can explain to humans its behaviors and assist human's wayfinding. This work is
expected to foster future efforts towards building more trustworthy and
socially-intelligent navigation robots.
