Demo
Overview
Object-Goal Navigation (ObjectNav) requires an agent to autonomously explore unknown environments and navigate toward target objects specified by semantic labels. While prior work has primarily studied zero-shot ObjectNav under 2D locomotion, extending it to aerial platforms with 3D locomotion remains underexplored.
AION is an end-to-end dual-policy reinforcement learning framework that decouples exploration and goal-reaching behaviors into two specialized policies. It enables vision-based aerial ObjectNav without relying on external localization or global maps.
Key Features
- Dual-Policy RL: Separate specialized policies for exploration and goal-reaching
- Vision-Based: RGB-D perception without external localization or global maps
- 3D Locomotion: Designed for aerial robots with superior maneuverability
- Sim-to-Real: Evaluated on AI2-THOR and IsaacSim with high-fidelity drone models