The growing integration of robots in shared environments-such as warehouses, shopping centres, and hospitals-demands a deep understanding of the underlying dynamics and human behaviours, including how, when, and where individuals engage in various activities and interactions. This knowledge goes beyond simple correlation studies and requires a more comprehensive causal analysis. By leveraging causal inference to model cause-and-effect relationships, we can better anticipate critical environmental factors and enable autonomous robots to plan and execute tasks more effectively. To this end, we propose a novel causality-based decision-making framework that reasons over a learned causal model to assist the robot in deciding when and how to complete a given task. In the examined use case-i.e., a warehouse shared with people-we exploit the causal model to estimate battery usage and human obstructions as factors influencing the robot's task execution. This reasoning framework supports the robot in making informed decisions about task timing and strategy. To achieve this, we developed also PeopleFlow, a new Gazebo-based simulator designed to model context-sensitive human-robot spatial interactions in shared workspaces. PeopleFlow features realistic human and robot trajectories influenced by contextual factors such as time, environment layout, and robot state, and can simulate a large number of agents. While the simulator is general-purpose, in this paper we focus on a warehouse-like environment as a case study, where we conduct an extensive evaluation benchmarking our causal approach against a non-causal baseline. Our findings demonstrate the efficacy of the proposed solutions, highlighting how causal reasoning enables autonomous robots to operate more efficiently and safely in dynamic environments shared with humans.

翻译：随着机器人在仓库、购物中心、医院等共享环境中的日益普及，要求我们深入理解其底层动态机制和人类行为模式，包括个体如何在何时、何地参与各种活动及互动。这类知识超越了简单的相关性研究，需要更全面的因果分析。通过运用因果推断建模因果关系，我们能更准确地预测关键环境因素，使自主机器人能更高效地规划并执行任务。为此，我们提出了一种基于因果推理的新型决策框架，该框架基于学习到的因果模型进行推理，帮助机器人决定何时以及如何完成给定任务。在研究的应用场景（即与人共存的仓库环境）中，我们利用因果模型估算电池消耗和人为障碍物等影响机器人任务执行的因素。这一推理框架支持机器人针对任务时机和策略做出明智决策。为实现这一目标，我们还开发了PeopleFlow——一款基于Gazebo的新型仿真器，用于模拟共享工作空间中上下文敏感的人-机器人空间交互。PeopleFlow能够生成受时间、环境布局和机器人状态等情境因素影响的真实人类与机器人轨迹，并可模拟大量智能体。尽管该仿真器具有通用性，本文以仓库类环境为例开展全面评估，将我们的因果方法与非因果基线进行对比。实验结果表明了所提方案的有效性，凸显了因果推理如何使自主机器人在与人类共享的动态环境中更高效、更安全地运行。