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

2024-05-05

arXiv.org

AI Summary

To address the challenge of simultaneously achieving rapid response and high success rates in motion planning, this paper proposes a problem-space adaptive experience library framework. Methodologically, it employs a classifier-embedded architecture, selects experience-classifier pairs via iterative greedy optimization to maximize coverage, integrates problem-space partitioning with joint learning, and defines an algorithm-agnostic interface to decouple the experience library from downstream adaptation modules. Key contributions include: (i) the first classifier-embedded experience library structure; (ii) a coverage-driven, active construction paradigm; and (iii) seamless integration of diverse adaptation algorithms—including both NLP-based and sampling-based approaches. Experiments demonstrate that the method significantly improves end-to-end planning success rates under millisecond-scale latency, effectively balancing global planning feasibility with local real-time responsiveness.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Multiagent Systems: Multiagent Planning
• Intelligent Robots: Motion and Path Planning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

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.

Problem

• Enhances motion planning efficiency
• Maximizes problem space coverage
• Integrates diverse adaptation algorithms

Innovation

• Iterative experience-classifier-pair addition
• Active selection for coverage maximization
• Adaptation-algorithm-agnostic integration