# Efficient Recovery Learning using Model Predictive Meta-Reasoning

arXiv: https://arxiv.org/abs/2209.13605 · HTML: https://mlsanity.com/paper/2209.13605/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones](https://mlsanity.com/paper/2010.15920/) (2020)
- [Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning](https://mlsanity.com/paper/1810.03043/) (2018)
- [Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies](https://mlsanity.com/paper/2107.08942/) (2021)
- [LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative Tasks](https://mlsanity.com/paper/2107.04775/) (2021)
- [Interpreting Contact Interactions to Overcome Failure in Robot Assembly Tasks](https://mlsanity.com/paper/2101.02725/) (2021)
- [Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation through Grounded Anomaly Classification and Recovery Policies](https://mlsanity.com/paper/1809.03979/) (2018)
- [Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations](https://mlsanity.com/paper/2204.08327/) (2022)

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

- [Practice Makes Perfect: Planning to Learn Skill Parameter Policies](https://mlsanity.com/paper/2402.15025/) (2024)
- [RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation](https://mlsanity.com/paper/2410.13979/) (2024)
- [Learning to Recover from Plan Execution Errors during Robot Manipulation: A Neuro-symbolic Approach](https://mlsanity.com/paper/2405.18948/) (2024)
- [SLAP: Shortcut Learning for Abstract Planning](https://mlsanity.com/paper/2511.01107/) (2025)
- [Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning](https://mlsanity.com/paper/2409.19226/) (2024)
- [Deliberate Practice: Learning Robot Skills under a Budget](https://mlsanity.com/paper/2608.13415/) (2026)
- [CARE: Experience-Guided Atomic Corrective Execution for Vision-Language-Action Policies](https://mlsanity.com/paper/2609.24118/) (2026)
- [UniIntervene++: An Adaptive Intervention Agent for Efficient Real-World Reinforcement Learning](https://mlsanity.com/paper/2610.03620/) (2026)

## Abstract (arXiv)

Operating under real world conditions is challenging due to the possibility of a wide range of failures induced by execution errors and state uncertainty. In relatively benign settings, such failures can be overcome by retrying or executing one of a small number of hand-engineered recovery strategies. By contrast, contact-rich sequential manipulation tasks, like opening doors and assembling furniture, are not amenable to exhaustive hand-engineering. To address this issue, we present a general approach for robustifying manipulation strategies in a sample-efficient manner. Our approach incrementally improves robustness by first discovering the failure modes of the current strategy via exploration in simulation and then learning additional recovery skills to handle these failures. To ensure efficient learning, we propose an online algorithm called Meta-Reasoning for Skill Learning (MetaReSkill) that monitors the progress of all recovery policies during training and allocates training resources to recoveries that are likely to improve the task performance the most. We use our approach to learn recovery skills for door-opening and evaluate them both in simulation and on a real robot with little fine-tuning. Compared to open-loop execution, our experiments show that even a limited amount of recovery learning improves task success substantially from 71% to 92.4% in simulation and from 75% to 90% on a real robot.