# Learning When to Quit: Meta-Reasoning for Motion Planning

arXiv: https://arxiv.org/abs/2103.04374 · HTML: https://mlsanity.com/paper/2103.04374/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2021.

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

- [A Survey on the Integration of Machine Learning with Sampling-based Motion Planning](https://mlsanity.com/paper/2211.08368/) (2022)
- [Risk-aware Meta-level Decision Making for Exploration Under Uncertainty](https://mlsanity.com/paper/2209.05580/) (2022)
- [DecTrain: Deciding When to Train a Monocular Depth DNN Online](https://mlsanity.com/paper/2410.02980/) (2024)

## Abstract (arXiv)

Anytime motion planners are widely used in robotics. However, the relationship between their solution quality and computation time is not well understood, and thus, determining when to quit planning and start execution is unclear. In this paper, we address the problem of deciding when to stop deliberation under bounded computational capacity, so called meta-reasoning, for anytime motion planning. We propose data-driven learning methods, model-based and model-free meta-reasoning, that are applicable to different environment distributions and agnostic to the choice of anytime motion planners. As a part of the framework, we design a convolutional neural network-based optimal solution predictor that predicts the optimal path length from a given 2D workspace image. We empirically evaluate the performance of the proposed methods in simulation in comparison with baselines.