RoboVAST: ロボットの大規模シナリオベース自動検証
RoboVAST: Automated Scenario-Based Validation of Robots at Scale
ロボットシステムの検証を自動化するフレームワークRoboVASTを提案し、5480のシナリオ設定と10万回以上の実行を通じて、系統的失敗と確率的異常を区別できることを示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Frederik Pasch, Samuel Wiest, Argentina Ortega, Nico Hochgeschwender
分類: cs.RO
原文アブストラクト
Validation of robotic systems critically depends on the operating conditions under which they are assessed. Scenario selection and variation are often manual, experience-driven, and difficult to scale, which harms reproducibility and weakens validation conclusions. We propose a scenario-based methodology that models scenarios compositionally and formalizes how these dimensions are varied, instantiated, executed, and interpreted. Building on this, we introduce RoboVAST, a framework that realizes declarative campaign specifications, plugin-based scenario generation, and scalable containerized execution with integrated result analysis. We demonstrate the approach with a navigation dataset comprising 5480 scenario configurations and over 100000 execution runs across five indoor maps with varied paths, sensor noise, software parameters, and obstacle settings, totaling more than 1800 hours of simulated operation and 1873 km traveled. Twenty repetitions per configuration allow us to distinguish systematic failures from stochastic anomalies.
