Policy-Level Recursive Self-Improvement for Embodied AI with a Criticality World Model
The framework uses a learned criticality model to guide data collection and deployment for embodied AI policies. It replaces redundant scenarios with diverse, failure-prone ones, significantly increasing training information density. This leads to substantial reductions in failure rates across various benchmarks by strategically selecting the most informative training data.