考虑行动成本的主动学习策略，提升机器人探索效率。
Cost-Aware Query Policies in Active Learning for Efficient Autonomous Robotic Exploration
- 结合动作成本设计高斯过程回归的主动学习策略
- 在不同地形映射任务中，距离约束下的不确定性度量误差最低
- 适合资源受限环境下需高效探索的机器人系统
在资源有限的任务中，高效数据采集至关重要。基于自动化决策的信息性路径规划可降低对环境目标精确表征的成本。以往主动学习方法未考虑回归问题的动作成本，或仅针对分类问题。本文分析了融合动作成本的高斯过程回归主动学习算法，在多种回归任务中评估其表现，包括不同模拟表面的地形映射，指标涵盖均方根误差、收敛所需样本数与移动距离，以及收敛时模型方差。结果表明，成本依赖型获取策略并未自然优化单位距离的信息增益；而传统不确定性度量配合距离约束，在轨迹距离上最小化了均方根误差。研究为在真实任务约束下整合动作成本与主动学习方法提供了重要洞见。
原文摘要 · Abstract (English)
In missions constrained by finite resources, efficient data collection is critical. Informative path planning, driven by automated decision-making, optimizes exploration by reducing the costs associated with accurate characterization of a target in an environment. Previous implementations of active learning did not consider the action cost for regression problems or only considered the action cost for classification problems. This paper analyzes an AL algorithm for Gaussian Process regression while incorporating action cost. The algorithm's performance is compared on various regression problems to include terrain mapping on diverse simulated surfaces along metrics of root mean square error, samples and distance until convergence, and model variance upon convergence. The cost-dependent acquisition policy doesn't organically optimize information gain over distance. Instead, the traditional uncertainty metric with a distance constraint best minimizes root-mean-square error over trajectory distance. This studys impact is to provide insight into incorporating action cost with AL methods to optimize exploration under realistic mission constraints.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。