# Learning Model Preconditions for Planning with Multiple Models

arXiv: https://arxiv.org/abs/2206.05573 · HTML: https://mlsanity.com/paper/2206.05573/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2022.

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

- [ELITE: Experiential Learning and Intent-Aware Transfer for Self-improving Embodied Agents](https://mlsanity.com/paper/2603.24018/) (2026)

## Abstract (arXiv)

Different models can provide differing levels of fidelity when a robot is planning. Analytical models are often fast to evaluate but only work in limited ranges of conditions. Meanwhile, physics simulators are effective at modeling complex interactions between objects but are typically more computationally expensive. Learning when to switch between the various models can greatly improve the speed of planning and task success reliability. In this work, we learn model deviation estimators (MDEs) to predict the error between real-world states and the states outputted by transition models. MDEs can be used to define a model precondition that describes which transitions are accurately modeled. We then propose a planner that uses the learned model preconditions to switch between various models in order to use models in conditions where they are accurate, prioritizing faster models when possible. We evaluate our method on two real-world tasks: placing a rod into a box and placing a rod into a closed drawer.