FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement
Failure-Aware Retry (FAR) helps robot policies recover from failures during testing by learning from past mistakes. It combines Failure-Contrastive Preference Adaptation to steer the policy away from bad actions with lightweight action perturbations to encourage exploration. This adaptive approach improves task success rates in both simulation and real-world environments.