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.

要旨より。当社による要約は作成中です。