  Human-robot collaboration, in which the robot intelligently assists the human
with the upcoming task, is an appealing objective. To achieve this goal, the
agent needs to be equipped with a fundamental collaborative navigation ability,
where the agent should reason human intention by observing human activities and
then navigate to the human's intended destination in advance of the human.
However, this vital ability has not been well studied in previous literature.
To fill this gap, we propose a collaborative navigation (CoNav) benchmark. Our
CoNav tackles the critical challenge of constructing a 3D navigation
environment with realistic and diverse human activities. To achieve this, we
design a novel LLM-based humanoid animation generation framework, which is
conditioned on both text descriptions and environmental context. The generated
humanoid trajectory obeys the environmental context and can be easily
integrated into popular simulators. We empirically find that the existing
navigation methods struggle in CoNav task since they neglect the perception of
human intention. To solve this problem, we propose an intention-aware agent for
reasoning both long-term and short-term human intention. The agent predicts
navigation action based on the predicted intention and panoramic observation.
The emergent agent behavior including observing humans, avoiding human
collision, and navigation reveals the efficiency of the proposed datasets and
agents.
