介入因果回路によるロボット動作の安全テストと障害回復
Interventional Causal Circuits for Safe Robot Action Testing and Failure Recovery
ロボットの動作計画がテストで拒否された際に、因果診断を用いて失敗原因のパラメータを特定し、修正値を提案する閉ループフレームワークを提案。確率木と因果回路を組み合わせ、追加学習なしで効率的に修正を行い、実験で失敗試行を最大37%削減した。
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著者: Naren Vasantakumaar, Tom Schierenbeck, Michael Beetz
分類: cs.RO, cs.AI
原文アブストラクト
Safe physical AI for robot actions are required not only likely to succeed but tested to be safe before execution. In practice, however, formal testing of motion parameters is computationally expensive, and the cost scales poorly with the dimensionality of the action space. When a proposed action is rejected by a tester, the naive response is to resample blindly until a passing candidate is found. This is wasteful, uninformative, and offers no convergence. We argue that rejection should instead trigger causal diagnosis: a principled identification of which action parameter caused the failure and what corrective value maximises the probability of passing testing under the interventional probability distribution. We propose a closed-loop framework that couples a Joint Probability Tree (JPT) with a Causal Circuit derived from a Marginal-Deterministic Variable Tree, enabling exact polytime computation without retraining, or additional data collection. The framework validates tractability of all interventional queries before the robot begins operating, and out-of-support candidates are detected and excluded from correction automatically. We perform experiments in a ROS2 simulation environment, and the framework demonstrates complementary roles across quality of distribution: under a high-quality JPT, the Causal Circuit reduces failed attempts by 10.3% and under a degraded JPT, it reduces total failed attempts by 37%. Every rejected plan produces a structured, interpretable causal report naming the primary cause variable, its observed value, and the recommended corrective region, supporting operator oversight and autonomous recovery without a separately trained failure model.
