  In this paper, we introduce the map-language navigation task where an agent
executes natural language instructions and moves to the target position based
only on a given 3D semantic map. To tackle the task, we design the
instruction-aware Path Proposal and Discrimination model (iPPD). Our approach
leverages map information to provide instruction-aware path proposals, i.e., it
selects all potential instruction-aligned candidate paths to reduce the
solution space. Next, to represent the map observations along a path for a
better modality alignment, a novel Path Feature Encoding scheme tailored for
semantic maps is proposed. An attention-based Language Driven Discriminator is
designed to evaluate path candidates and determine the best path as the final
result. Our method can naturally avoid error accumulation compared with
single-step greedy decision methods. Comparing to a single-step imitation
learning approach, iPPD has performance gains above 17% on navigation success
and 0.18 on path matching measurement nDTW in challenging unseen environments.
