From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents
Simulated environments enable scalable training data generation for embodied agents, but more data does not necessarily provide what a particular agent needs to learn. Existing environment-generation pipelines largely optimize generic properties such as realism, diversity, or task coverage, while different pretrained policies can fail for different environmental reasons. We therefore introduce WEDGE, a framework for weakness-guided navigation learning in which the failure modes of a pretrained agent guide the generation of training environments at each adaptation round. Given a pretrained policy, WEDGE uses an LLM to propose executable weakness features from contrasting successful and fai...
| Authors | Yue Zhang, Han Lin, Zun Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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