论文标题
手动操纵的经验估计是可恢复的：在日常活动中迈出个性化和可解释的机器人支持的一步
Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities
论文作者
论文摘要
机器人系统的关键挑战是找出另一个代理的行为。得出正确推断的能力对于从例子中得出人类行为至关重要。 当（混淆）因素无法通过实验控制（观察证据）时，处理正确的推论尤其具有挑战性。因此，依赖于相关风险的推论的机器人对证据的偏见解释。 我们建议将机器人配备必要的工具，以对人进行观察性研究。具体而言，我们提出和探索具有非参数估计器的结构因果模型的可行性，以在虚拟厨房场景中对物体操纵的背景下得出有关手部行为的经验估计。特别是，我们将重点放在（较弱的）部分混杂条件下（仅涵盖某些因素的模型）和估计量以数百个样本而不是典型的成千上万顺序面对估计器。研究这些条件探讨了方法的边界及其生存能力。 尽管有挑战性的条件，但从验证数据中推断出的估计值正确。此外，这些估计值与三个估计器一致的三种反驳策略是稳定的。此外，两个个体的因果量揭示了检测正面和负面影响的方法的敏感性。 该方法的有效性，稳定性和解释性令人鼓舞，并作为进一步研究的基础。
A key challenge for robotic systems is to figure out the behavior of another agent. The capability to draw correct inferences is crucial to derive human behavior from examples. Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally (observational evidence). For this reason, robots that rely on inferences that are correlational risk a biased interpretation of the evidence. We propose equipping robots with the necessary tools to conduct observational studies on people. Specifically, we propose and explore the feasibility of structural causal models with non-parametric estimators to derive empirical estimates on hand behavior in the context of object manipulation in a virtual kitchen scenario. In particular, we focus on inferences under (the weaker) conditions of partial confounding (the model covering only some factors) and confront estimators with hundreds of samples instead of the typical order of thousands. Studying these conditions explores the boundaries of the approach and its viability. Despite the challenging conditions, the estimates inferred from the validation data are correct. Moreover, these estimates are stable against three refutation strategies where four estimators are in agreement. Furthermore, the causal quantity for two individuals reveals the sensibility of the approach to detect positive and negative effects. The validity, stability and explainability of the approach are encouraging and serve as the foundation for further research.
