How do you design synthetic navigation environments without inducing geometry-based shortcut learning?

I’m working with synthetic 2D navigation environments for testing learning-based path planning methods, where the agent must trade off between different criteria like efficiency, safety, and smoothness.

One issue I keep running into is that the structure of the environment itself can unintentionally create shortcuts in learning. For example, if certain geometric patterns (like narrow corridors or open spaces) consistently align with specific outcomes, the model tends to pick up on those correlations rather than learning the underlying decision-making problem. If I randomize everything too much, though, the environments lose meaningful structure and stop being useful for evaluation or learning.

I’m trying to understand what the standard practice is here. How do people design navigation environments that still have meaningful structure without embedding obvious visual shortcuts, and how do you avoid models learning direct “geometry → outcome” mappings instead of more general reasoning? In practice, is it better to use structured layouts (corridors, bottlenecks, etc.), or to rely on adding stochastic cost/risk layers on top of simpler geometry? Are there known approaches for balancing structure and randomness in a principled way, and are there standard algorithms, generators, or libraries commonly used for building these kinds of synthetic navigation environments?

Would appreciate any references or practical insights from motion planning or RL practice.