# Task-Oriented Active Learning of Model Preconditions for Inaccurate Dynamics Models

arXiv: https://arxiv.org/abs/2401.04007 · HTML: https://mlsanity.com/paper/2401.04007/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2024.

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

- [ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation](https://mlsanity.com/paper/2506.04646/) (2025)

## Abstract (arXiv)

When planning with an inaccurate dynamics model, a practical strategy is to restrict planning to regions of state-action space where the model is accurate: also known as a textitmodel precondition. Empirical real-world trajectory data is valuable for defining data-driven model preconditions regardless of the model form (analytical, simulator, learned, etc...). However, real-world data is often expensive and dangerous to collect. In order to achieve data efficiency, this paper presents an algorithm for actively selecting trajectories to learn a model precondition for an inaccurate pre-specified dynamics model. Our proposed techniques address challenges arising from the sequential nature of trajectories, and potential benefit of prioritizing task-relevant data. The experimental analysis shows how algorithmic properties affect performance in three planning scenarios: icy gridworld, simulated plant watering, and real-world plant watering. Results demonstrate an improvement of approximately 80% after only four real-world trajectories when using our proposed techniques.