Robot-WATCHDOG：物体中心グラフ表現による失敗検出
Robot-WATCHDOG : failure detection through object-centric graph representation
操作シーンを物体中心の時空間グラフで表現し、正常デモのみから自己教師ありで失敗を検出する枠組みを提案。幾何偏差と関係整合性に基づくTPCと、将来の物体状態を予測するGnnTの2つのスコアを検証した。
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著者: Quentin Rolland, Fabrice Mayran de Chamisso, Jean-Baptiste Mouret
分類: cs.RO
原文アブストラクト
Reliable real-time failure detection is critical for deploying learned robotic policies, yet defining failures explicitly is infeasible in open-world settings. This motivates failure detection from nominal demonstrations alone. Existing methods, however, struggle to balance robustness to benign variations with the ability to capture semantic and interaction-level errors. We introduce Robot-WATCHDOG, a self-supervised framework that models manipulation scenes as object-centric spatio-temporal graphs. Building on this representation, we investigate two complementary failure scores: Trajectory Projection and Correlation (TPC), a non-parametric measure of geometric deviation and relational consistency, and GnnT, a Graph Attention Network with a Transformer that predicts future object states. We evaluate both on a public benchmark and on a new dataset of semantically complex manipulation tasks, across six tasks and ten random seeds. Both scores outperform image-based failure detectors, particularly on semantic and interaction-level failures. TPC provides the strongest performance in AUPR and MCC on both dataset averages, with low variance across seeds. Qualitative results are made available at https://cea-list.github.io/Robot-WATCHDOG/.