# Asking for Help: Failure Prediction in Behavioral Cloning through Value Approximation

arXiv: https://arxiv.org/abs/2302.04334 · HTML: https://mlsanity.com/paper/2302.04334/

## Key facts

- Cited by 16 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.
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 8 Feb 2023.

## Placed in

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

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1604.00923/) (2016)
- [Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1911.06854/) (2019)
- [Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes](https://mlsanity.com/paper/1908.08526/) (2019)
- [EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning](https://mlsanity.com/paper/1807.08364/) (2018)
- [Benchmarks for Deep Off-Policy Evaluation](https://mlsanity.com/paper/2103.16596/) (2021)
- [Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation](https://mlsanity.com/paper/2112.05251/) (2021)
- [GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation](https://mlsanity.com/paper/1803.03254/) (2018)
- [Bayesian Imitation Learning for End-to-End Mobile Manipulation](https://mlsanity.com/paper/2202.07600/) (2022)
- [Practical Imitation Learning in the Real World via Task Consistency Loss](https://mlsanity.com/paper/2202.01862/) (2022)
- [Combined Task and Action Learning from Human Demonstrations for Mobile Manipulation Applications](https://mlsanity.com/paper/1908.10184/) (2019)

## Built on by (most cited first, 16 of 16)

- [SAFE: Multitask Failure Detection for Vision-Language-Action Models](https://mlsanity.com/paper/2506.09937/) (2025)
- [Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies](https://mlsanity.com/paper/2503.08558/) (2025)
- [Model-Based Runtime Monitoring with Interactive Imitation Learning](https://mlsanity.com/paper/2310.17552/) (2023)
- [Multi-Task Interactive Robot Fleet Learning with Visual World Models](https://mlsanity.com/paper/2410.22689/) (2024)
- [CycleVLA: Proactive Self-Correcting Vision-Language-Action Models via Subtask Backtracking and Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2601.02295/) (2026)
- [Human-assisted Robotic Policy Refinement via Action Preference Optimization](https://mlsanity.com/paper/2506.07127/) (2025)
- [SafeMimic: Towards Safe and Autonomous Human-to-Robot Imitation for Mobile Manipulation](https://mlsanity.com/paper/2506.15847/) (2025)
- [Failure Identification in Imitation Learning Via Statistical and Semantic Filtering](https://mlsanity.com/paper/2604.13788/) (2026)
- [FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement](https://mlsanity.com/paper/2607.01111/) (2026)
- [ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies](https://mlsanity.com/paper/2606.08508/) (2026)
- [SAFECAST: Robust Failure Detection for VLA Policies with Contrast-Set Training and Calibration](https://mlsanity.com/paper/2608.04246/) (2026)
- [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)
- [Robots that Suggest Safe Alternatives](https://mlsanity.com/paper/2409.09883/) (2024)
- [ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning](https://mlsanity.com/paper/2508.05310/) (2025)
- [PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation](https://mlsanity.com/paper/2606.08414/) (2026)

## Abstract (arXiv)

Recent progress in end-to-end Imitation Learning approaches has shown promising results and generalization capabilities on mobile manipulation tasks. Such models are seeing increasing deployment in real-world settings, where scaling up requires robots to be able to operate with high autonomy, i.e. requiring as little human supervision as possible. In order to avoid the need for one-on-one human supervision, robots need to be able to detect and prevent policy failures ahead of time, and ask for help, allowing a remote operator to supervise multiple robots and help when needed. However, the black-box nature of end-to-end Imitation Learning models such as Behavioral Cloning, as well as the lack of an explicit state-value representation, make it difficult to predict failures. To this end, we introduce Behavioral Cloning Value Approximation (BCVA), an approach to learning a state value function based on and trained jointly with a Behavioral Cloning policy that can be used to predict failures. We demonstrate the effectiveness of BCVA by applying it to the challenging mobile manipulation task of latched-door opening, showing that we can identify failure scenarios with with 86% precision and 81% recall, evaluated on over 2000 real world runs, improving upon the baseline of simple failure classification by 10 percentage-points.