Vision-and-Language Navigation (VLN) aims to develop embodied agents that navigate based on human instructions. However, current VLN frameworks often rely on static environments and optimal expert supervision, limiting their real-world applicability. To address this, we introduce Human-Aware Vision-and-Language Navigation (HA-VLN), extending traditional VLN by incorporating dynamic human activities and relaxing key assumptions.

SeqComm are guaranteed to improve monotonically and converge.

Imitating humans' mental world knowledge model which provides global prior knowledge before the task and maintains local dynamic knowledge during the task, in this paper, we introduce parametric W orld K nowledge M odel ( WKM) to facilitate agent

Consequently, it is uncertain whether the generated modalities sufficiently represent the diversity of future behaviors.

Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings.