FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement
Robot policies inevitably encounter failures when deployed in real environments. Naive retries often repeat the same mistakes, while many existing recovery methods rely on human intervention. In this paper, we propose Failure-Aware Retry (FAR), a framework that enables robots to learn from previous failures at test time, adapt their behavior accordingly, and eventually complete the task autonomously. FAR combines Failure-Contrastive Preference Adaptation, which constructs preference learning data from failures to steer the policy away from previously unsuccessful behaviors, with lightweight ac
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- LinkedLinked via unknownRetrace-1.5B →
- LinkedLinked via arxiv author · 85%Haoran Hao →
“FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement”
- LinkedLinked via arxiv author · 85%Shahram Najam Syed →
“FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement”
- LinkedLinked via arxiv author · 85%Jeffrey Ichnowski →
“FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement”
- LinkedLinked via arxiv author · 85%Jeff Schneider →
“FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement”
- FuzzyOverlapping authors or contributors · 62%mastra-ai/mastra →
“Shared author/contributor keys: schneider”
- FuzzyOverlapping authors or contributors · 62%Fincept-Corporation/FinceptTerminal →
“Shared author/contributor keys: schneider”