# InFeR: Informed Failure Resilience in Learned Visual Navigation Control

arXiv: https://arxiv.org/abs/2510.24680 · HTML: https://mlsanity.com/paper/2510.24680/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Language-conditioned policies and VLAs, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 28 Oct 2025.

## Placed in

- Offline RL and imitation learning from data / [Language-conditioned policies and VLAs](https://mlsanity.com/offlinerl/language-conditioned-policies-and-vlas/)

## Built on by (most cited first, 1 of 1)

- [Rewind-IL: Online Failure Detection and State Respawning for Imitation Learning](https://mlsanity.com/paper/2604.16683/) (2026)

## Abstract (arXiv)

While imitation learning (IL) has enabled successful visual navigation in many common environments, IL policies are prone to unpredictable failures under out-of-distribution (OOD) scenarios. This necessitates failure-resilient policies, which not only detect failures, but also recognise their sources and recover from them autonomously. We propose InFeR, a general framework for building IL policies with informed failure resilience without failure or recovery demonstrations. InFeR retrains an IL policy with a Variational Information Bottleneck (VIB) loss to structure its latent space for OOD failure detection. It applies a visual explainability technique, Grad-CAM, to localise an image region as the source of failure and inform a heuristic policy for recovery. All these are achieved without requiring additional training data. Real-world experiments show that InFeR enables informed failure recovery across two different policy architectures, yielding robust long-range navigation in complex environments.