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：随着机器人在共享环境（如仓库、购物中心和医院）中的日益普及，亟需深入理解底层动态机制与人类行为模式，包括个体在何时、何地以及如何进行各类活动与交互。这种认知需求超越了简单的相关性研究，需要进行更全面的因果分析。通过利用因果推断建模因果关系，我们能够更准确地预测关键环境因素，使自主机器人能够更有效地规划与执行任务。为此，本文提出一种新颖的基于因果关系的决策框架，该框架通过对学习到的因果模型进行推理，辅助机器人决定执行特定任务的时机与方式。在研究的应用场景（即与人共享的仓库环境）中，我们利用因果模型将电池消耗与人员阻碍作为影响机器人任务执行的关键因素进行量化评估。该推理框架支持机器人基于综合信息对任务时机与策略作出决策。为实现这一目标，我们同时开发了PeopleFlow——一个基于Gazebo的新型仿真器，专门用于模拟共享工作空间中情境敏感的人机空间交互。PeopleFlow具备受时间、环境布局与机器人状态等情境因素影响的逼真人类与机器人轨迹，并能模拟大规模智能体。虽然该仿真器具有通用性，但本文聚焦于类仓库环境作为案例研究，通过大量实验将所提出的因果方法与无因果基线进行对比评估。研究结果验证了所提解决方案的有效性，揭示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实现更高效、更安全的运行。