# Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation through Grounded Anomaly Classification and Recovery Policies

arXiv: https://arxiv.org/abs/1809.03979 · HTML: https://mlsanity.com/paper/1809.03979/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 6 of 6)

- [Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery](https://mlsanity.com/paper/2101.01625/) (2021)
- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [Efficient Recovery Learning using Model Predictive Meta-Reasoning](https://mlsanity.com/paper/2209.13605/) (2022)
- [What Breaks Embodied AI Security:LLM Vulnerabilities, CPS Flaws,or Something Else?](https://mlsanity.com/paper/2602.17345/) (2026)
- [HiTac-WAM: A Hierarchical Tactile World Action Model for Contact-Rich Robot Manipulation](https://mlsanity.com/paper/2608.19574/) (2026)
- [Diffusing Trajectory Optimization Problems for Recovery During Multi-Finger Manipulation](https://mlsanity.com/paper/2510.07030/) (2025)

## Abstract (arXiv)

Robot manipulation is increasingly poised to interact with humans in co-shared workspaces. Despite increasingly robust manipulation and control algorithms, failure modes continue to exist whenever models do not capture the dynamics of the unstructured environment. To obtain longer-term horizons in robot automation, robots must develop introspection and recovery abilities. We contribute a set of recovery policies to deal with anomalies produced by external disturbances as well as anomaly classification through the use of non-parametric statistics with memoized variational inference with scalable adaptation. A recovery critic stands atop of a tightly-integrated, graph-based online motion-generation and introspection system that resolves a wide range of anomalous situations. Policies, skills, and introspection models are learned incrementally and contextually in a task. Two task-level recovery policies: re-enactment and adaptation resolve accidental and persistent anomalies respectively. The introspection system uses non-parametric priors along with Markov jump linear systems and memoized variational inference with scalable adaptation to learn a model from the data. Extensive real-robot experimentation with various strenuous anomalous conditions is induced and resolved at different phases of a task and in different combinations. The system executes around-the-clock introspection and recovery and even elicited self-recovery when misclassifications occurred.