# Learning Skills to Patch Plans Based on Inaccurate Models

arXiv: https://arxiv.org/abs/2009.13732 · HTML: https://mlsanity.com/paper/2009.13732/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 29 Sep 2020.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Built on by (most cited first, 3 of 3)

- [Coarse-to-Fine for Sim-to-Real: Sub-Millimetre Precision Across Wide Task Spaces](https://mlsanity.com/paper/2105.11283/) (2021)
- [RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation](https://mlsanity.com/paper/2410.13979/) (2024)
- [UniIntervene++: An Adaptive Intervention Agent for Efficient Real-World Reinforcement Learning](https://mlsanity.com/paper/2610.03620/) (2026)

## Abstract (arXiv)

Planners using accurate models can be effective for accomplishing manipulation tasks in the real world, but are typically highly specialized and require significant fine-tuning to be reliable. Meanwhile, learning is useful for adaptation, but can require a substantial amount of data collection. In this paper, we propose a method that improves the efficiency of sub-optimal planners with approximate but simple and fast models by switching to a model-free policy when unexpected transitions are observed. Unlike previous work, our method specifically addresses when the planner fails due to transition model error by patching with a local policy only where needed. First, we use a sub-optimal model-based planner to perform a task until model failure is detected. Next, we learn a local model-free policy from expert demonstrations to complete the task in regions where the model failed. To show the efficacy of our method, we perform experiments with a shape insertion puzzle and compare our results to both pure planning and imitation learning approaches. We then apply our method to a door opening task. Our experiments demonstrate that our patch-enhanced planner performs more reliably than pure planning and with lower overall sample complexity than pure imitation learning.