# CoverLib: Classifiers-equipped Experience Library by Iterative Problem Distribution Coverage Maximization for Domain-tuned Motion Planning

arXiv: https://arxiv.org/abs/2405.02968 · HTML: https://mlsanity.com/paper/2405.02968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 58 references that are in the map.
- First posted on arXiv 5 May 2024.

## Builds on (most cited first, 26 of 26)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [OSQP: An Operator Splitting Solver for Quadratic Programs](https://mlsanity.com/paper/1711.08013/) (2017)
- [Learning Sampling Distributions for Robot Motion Planning](https://mlsanity.com/paper/1709.05448/) (2017)
- [Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners](https://mlsanity.com/paper/1907.06013/) (2019)
- [Robot Motion Planning in Learned Latent Spaces](https://mlsanity.com/paper/1807.10366/) (2018)
- [Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs](https://mlsanity.com/paper/1405.5848/) (2014)
- [Real-time Perception meets Reactive Motion Generation](https://mlsanity.com/paper/1703.03512/) (2017)
- [Fast Marching Tree: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions](https://mlsanity.com/paper/1306.3532/) (2013)
- [Sampling-Based Motion Planning: A Comparative Review](https://mlsanity.com/paper/2309.13119/) (2023)
- [Learned Critical Probabilistic Roadmaps for Robotic Motion Planning](https://mlsanity.com/paper/1910.03701/) (2019)
- [Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions](https://mlsanity.com/paper/2010.15335/) (2020)
- [cmaes: A Simple yet Practical Python Library for CMA-ES](https://mlsanity.com/paper/2402.01373/) (2024)
- [Experience-Based Planning with Sparse Roadmap Spanners](https://mlsanity.com/paper/1410.1950/) (2014)
- [Memory of Motion for Warm-starting Trajectory Optimization](https://mlsanity.com/paper/1907.01474/) (2019)
- [Using Local Experiences for Global Motion Planning](https://mlsanity.com/paper/1903.08693/) (2019)
- [Provably Constant-time Planning and Replanning for Real-time Grasping Objects off a Conveyor Belt](https://mlsanity.com/paper/2101.07148/) (2021)
- [Learning to Retrieve Relevant Experiences for Motion Planning](https://mlsanity.com/paper/2204.08550/) (2022)
- [Learning the Problem-Optimum Map: Analysis and Application to Global Optimization in Robotics](https://mlsanity.com/paper/1605.04636/) (2016)
- [Alternative Paths Planner (APP) for Provably Fixed-time Manipulation Planning in Semi-structured Environments](https://mlsanity.com/paper/2012.14970/) (2020)
- [Path Planning for Manipulation using Experience-driven Random Trees](https://mlsanity.com/paper/2103.00448/) (2021)
- [AIT* and EIT*: Asymmetric bidirectional sampling-based path planning](https://mlsanity.com/paper/2111.01877/) (2021)
- [Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts](https://mlsanity.com/paper/1803.02493/) (2018)
- [Learning-based Collision-free Planning on Arbitrary Optimization Criteria in the Latent Space through cGANs](https://mlsanity.com/paper/2202.13062/) (2022)
- [Memory Clustering using Persistent Homology for Multimodality- and Discontinuity-Sensitive Learning of Optimal Control Warm-starts](https://mlsanity.com/paper/2010.01024/) (2020)

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

- [Path Database Guidance for Motion Planning](https://mlsanity.com/paper/2504.05550/) (2025)

## Abstract (arXiv)

Library-based methods are known to be very effective for fast motion planning by adapting an experience retrieved from a precomputed library. This article presents CoverLib, a principled approach for constructing and utilizing such a library. CoverLib iteratively adds an experience-classifier-pair to the library, where each classifier corresponds to an adaptable region of the experience within the problem space. This iterative process is an active procedure, as it selects the next experience based on its ability to effectively cover the uncovered region. During the query phase, these classifiers are utilized to select an experience that is expected to be adaptable for a given problem. Experimental results demonstrate that CoverLib effectively mitigates the trade-off between plannability and speed observed in global (e.g. sampling-based) and local (e.g. optimization-based) methods. As a result, it achieves both fast planning and high success rates over the problem domain. Moreover, due to its adaptation-algorithm-agnostic nature, CoverLib seamlessly integrates with various adaptation methods, including nonlinear programming-based and sampling-based algorithms.