Breaking Success-Path Collapse: Advancing Aerial Vision-and-Language Navigation via Active Environment Shaping

Simulation-based trajectory synthesis has emerged as a promising route towards scalable training data for aerial vision-and-language navigation. However, existing generators, whether search-based planning or reinforcement learning algorithms, share a failure mode that we identify and term Success-Path Collapse. Specifically, trajectories converge onto a narrow set of near-optimal routes. This behavioral redundancy, masked by high task success, silently propagates into downstream training data and compromises the robustness of learned navigation policies. In this work, we propose a novel perspective to actively restructure the environment to elicit multimodal trajectory generation, while remaining fully compatible with existing data synthesis pipelines. We instantiate this perspective with Trajectory-Aware Environment Perturbation (TAEP), which diagnoses collapse structures from successful trajectories, including biased junction exits, over-visited hotspots, and redundant route clusters, and places targeted virtual obstacles that naturally turn the pursuit of successful trajectories into continual route discovery. Experiments in AirSim urban scenes show that TAEP achieves the strongest overall trajectory diversity across a suite of diagnostic metrics, outperforming vanilla PPO, intrinsic-reward exploration, and random perturbation. TAEP-generated demonstrations translate these gains into improved closed-loop VLA navigation, more than doubling vanilla PPO's success rate on the longest tasks and improving mean success rate by 6.5 percentage points over the strongest evaluated baseline. Code and data are available at https://anonymous.4open.science/r/TAEP-ICLR-22E1/.