# Fighting Failures with FIRE: Failure Identification to Reduce Expert Burden in Intervention-Based Learning

arXiv: https://arxiv.org/abs/2007.00245 · HTML: https://mlsanity.com/paper/2007.00245/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 1 Jul 2020.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Built on by (most cited first, 8 of 8)

- [Human-in-the-Loop Imitation Learning using Remote Teleoperation](https://mlsanity.com/paper/2012.06733/) (2020)
- [SAFE: Multitask Failure Detection for Vision-Language-Action Models](https://mlsanity.com/paper/2506.09937/) (2025)
- [Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks](https://mlsanity.com/paper/2301.00051/) (2022)
- [Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations](https://mlsanity.com/paper/2104.13907/) (2021)
- [SAFECAST: Robust Failure Detection for VLA Policies with Contrast-Set Training and Calibration](https://mlsanity.com/paper/2608.04246/) (2026)
- [ILeSiA: Interactive Learning of Robot Situational Awareness from Camera Input](https://mlsanity.com/paper/2409.20173/) (2024)
- [Temporal Difference Calibration in Sequential Tasks: Application to Vision-Language-Action Models](https://mlsanity.com/paper/2604.20472/) (2026)
- [VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models](https://mlsanity.com/paper/2605.29605/) (2026)

## Abstract (arXiv)

Supervised imitation learning, also known as behavioral cloning, suffers from distribution drift leading to failures during policy execution. One approach to mitigate this issue is to allow an expert to correct the agent's actions during task execution, based on the expert's determination that the agent has reached a `point of no return.' The agent's policy is then retrained using this new corrective data. This approach alone can enable high-performance agents to be learned, but at a substantial cost: the expert must vigilantly observe execution until the policy reaches a specified level of success, and even at that point, there is no guarantee that the policy will always succeed. To address these limitations, we present FIRE (Failure Identification to Reduce Expert Burden in intervention-based learning), a system that can predict when a running policy will fail, halt its execution, and request a correction from the expert. Unlike existing approaches that learn only from expert data, our approach learns from both expert and non-expert data, akin to adversarial learning. We demonstrate experimentally for a series of challenging manipulation tasks that our method is able to recognize state-action pairs that lead to failures. This permits seamless integration into an intervention-based learning system, where we show an order-of-magnitude gain in sample efficiency compared with a state-of-the-art inverse reinforcement learning method and dramatically improved performance over an equivalent amount of data learned with behavioral cloning.