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How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics

机译：机器人应该如何评估风险？朝向机器人学的公理风险理论

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Safe planning and decision making under uncertainty are widely regarded as central challenges in enabling robots to successfully operate in real-world environments. By far the most common conceptual framework for addressing these challenges is to assign costs to stochastic outcomes and then to use the expected value of the resulting cost distribution as a quantity that "summarizes" the value of a decision. Such a quantity can then be optimized, or bounded within a constrained formulation. However, in settings where risk has to be accounted for, this choice is rarely well justified beyond the mathematical convenience it affords. For example, imagine a safety-critical application such as autonomous driving; would a passenger riding in an autonomous car be happy to do so if she was told that the average behavior of the car is not to crash? While one can introduce some degree of risk sensitivity (i.e., sensitivity to the tails of the cost distribution) in the expected cost framework by simply shaping the cost function, this can quickly turn into an exercise in "cost function hacking". Unless one is careful about the way one shapes the cost function, this can lead to the robot behaving in an irrational manner. The common alternative approach, aimed at promoting risk sensitivity, is to consider a worst-case assessment of the distribution of stochastic outcomes. In practice, however, such an assessment can often be quite conservative: an autonomous car whose goal is to never crash would never leave the garage.
机译：在不确定性下的安全规划和决策被广泛被认为是在实现机器人在现实世界环境中成功运行的中央挑战。到目前为止，解决这些挑战的最常见的概念框架是为随机结果分配成本，然后使用所得成本分配的预期值作为“总结”决定的价值。然后可以优化这种量，或者在约束的制剂内界定。但是，在必须占风险的环境中，这种选择很少有理由超出它所提供的数学便利性。例如，想象一个安全关键的应用，如自主驾驶;如果她被告知汽车的平均行为不崩溃，那么乘客会乘坐自治车骑行，这是乐意这样做吗？虽然通过简单地塑造成本函数，但可以在预期的成本框架中引入一定程度的风险敏感度（即，对成本分布的尾部的敏感性），这可以在“成本函数黑客”中迅速变成练习。除非一个人仔细致力于一种形状成本函数，否则这可能导致机器人以不合理的方式行为。旨在促进风险敏感性的常见替代方法是考虑对随机结果分布的最坏情况。然而，在实践中，这种评估通常可以是非常保守的：一个自治车，其目标是永不崩溃的自治车永远不会离开车库。

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