AI 中文总结
研究机器人动作安全测试及故障恢复问题，提出耦合联合概率树与因果电路并无需重新训练或额外数据收集的闭环框架，经实验验证该框架能减少失败尝试，且被拒计划可生成因果报告支持监督与恢复。
AI 中文摘要
安全的物理人工智能机器人动作不仅需要可能成功，还需在执行前经过安全测试。然而在实践中，运动参数的形式化测试计算成本高昂，且成本随动作空间维度扩展性差。当提议动作被测试器拒绝时，盲目重采样是浪费且无信息的，也无法收敛。我们认为应触发因果诊断来确定导致失败的动作参数及校正值。为此提出一个闭环框架，将联合概率树与因果电路耦合，能精确计算且无需重新训练或额外数据收集。该框架在机器人运行前验证所有干预查询的可处理性，自动检测并排除无支持的候选校正。我们在ROS2模拟环境中进行实验，结果表明在高质量联合概率树下，因果电路将失败尝试减少10.3%，在退化联合概率树下减少37%。每个被拒绝的计划都会生成结构化、可解释的因果报告，支持操作员监督和自主恢复，无需单独训练的故障模型。
英文摘要
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.
Comments1st IJCAI Workshop on Safe Physical AI IJCAI 2026 workshop on Safe Physical AI
