Autonomous robots are required to reason about the behaviour of dynamic agents in their environment. To this end, many approaches assume that causal models describing the interactions of agents are given a priori. However, in many application domains such models do not exist or cannot be engineered. Hence, the learning (or discovery) of high-level causal structures from low-level, temporal observations is a key problem in AI and robotics. However, the application of causal discovery methods to scenarios involving autonomous agents remains in the early stages of research. While a number of methods exist for performing causal discovery on time series data, these usually rely upon assumptions such as sufficiency and stationarity which cannot be guaranteed in interagent behavioural interactions in the real world. In this paper we are applying contemporary observation-based temporal causal discovery techniques to real world and synthetic driving scenarios from multiple datasets. Our evaluation demonstrates and highlights the limitations of state of the art approaches by comparing and contrasting the performance between real and synthetically generated data. Finally, based on our analysis, we discuss open issues related to causal discovery on autonomous robotics scenarios and propose future research directions for overcoming current limitations in the field.

翻译：自主机器人需要推断其环境中动态智能体的行为。为此，许多方法假设描述智能体间交互的因果模型是预先给定的。然而在许多应用场景中，这类模型要么不存在，要么无法通过工程手段构建。因此，从低层时间观测数据中学习（或发现）高层因果结构已成为人工智能与机器人领域的核心问题。但将因果发现方法应用于涉及自主智能体的场景仍处于研究初期。尽管已有多种针对时间序列数据的因果发现方法，但这些方法通常依赖于充分性和平稳性等假设，而这些假设在真实世界的智能体行为交互中无法得到保证。本文基于多数据集，将当代基于观测的时间因果发现技术应用于真实与合成的驾驶场景。通过对比真实数据与合成数据上的算法表现，本评估展示并揭示了现有前沿方法的局限性。最后根据分析结果，我们讨论了自主机器人场景中因果发现面临的开放性问题，并提出了未来研究方向以突破当前领域局限。