FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement

2026-07-01

AI Summary

This work addresses the challenge of robotic policy degradation in real-world environments due to repeated failures, a problem often mitigated by manual intervention in existing approaches. The authors propose the Failure-Aware Retry (FAR) framework, which, during deployment, leverages observed failure cases to construct contrastive preference signals that guide the policy away from ineffective actions. FAR integrates lightweight action perturbations to encourage local exploration and employs online replay of successfully recovered trajectories for continual policy refinement. Notably, the method enables autonomous recovery without additional training, substantially improving both data efficiency and robustness. Experimental results demonstrate that FAR increases average task success rates by 17.6% in simulation and 11.7% on physical robots, with particularly strong performance under constraints on reset counts and time steps.

Abstract

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 action perturbations during retries to encourage local exploration. We further incorporate successful recovery trajectories into a training loop for continual policy improvement. Experiments in both simulation and real-world manipulation tasks show that FAR substantially improves success rates and robustness, with average gains of 17.6% over the standard diffusion policy in simulation and 11.7% in the real world. In addition, FAR significantly improves data efficiency under both reset and timestep budgets during continual policy improvement by exploiting informative failure cases.

Problem

• robot failures
• test-time recovery
• autonomous retry
• policy improvement
• real-world deployment

Innovation

• Failure-Aware Retry
• Preference Adaptation
• Test-Time Recovery
• Continual Policy Improvement
• Action Perturbation