Core Concepts
CoverLib, a principled approach for constructing and utilizing an experience library, effectively mitigates the trade-off between plannability and speed observed in global and local motion planning methods, achieving both fast planning and high success rates over the problem domain.
Abstract
The content presents CoverLib, a novel approach for constructing and utilizing an experience library for efficient motion planning. The key components are:
Iterative Library Construction:
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.
The iterative process is an active procedure, selecting the next experience based on its ability to effectively cover the uncovered region.
Adaptation-Algorithm-Agnostic Design:
CoverLib seamlessly integrates with various adaptation methods, including nonlinear programming-based and sampling-based algorithms.
This allows CoverLib to leverage the strengths of different adaptation algorithms.
Domain-Tuned Library:
The active iteration in the library construction process makes CoverLib domain-tuned, concentrating experiences on the more challenging parts of the problem distribution and focusing on dimensions with greater impact on adaptation.
This makes CoverLib less susceptible to the curse of dimensionality compared to passive library construction methods.
Numerical Experiments:
The authors evaluate CoverLib on three motion planning problem domains, including a kinodynamic planning task for a double integrator system.
CoverLib outperforms global planners and nearest-neighbor-based library methods in terms of planning performance and library growth efficiency.
Stats
The content does not provide any specific numerical data or metrics. It focuses on describing the CoverLib algorithm and its key components.
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