Causality in robotics aims to produce more interpretable and flexible robot behaviours by enabling robots to predict the consequences of their actions; however, deploying causal models with existing systems (e.g., navigation) operating in real environments remains understudied. This paper addresses the challenging problem of transferring causal models in real-robot experiments for a navigation scenario. We study this problem in two ways: (i) using the causal model as an offline evaluation module that predicts the competence of recorded real-robot navigation trajectories and relates it to quantitative navigation performance, and (ii) using the causal model as an online adaptation module that intervenes when the predicted competence of the default navigation is low. We validate our approach in a physical service robot that patrols around corridors. We show that the predicted competence correlates positively with path efficiency, and negatively with path irregularities (suboptimal behaviour). The model predictions also show strong agreement with human annotations (Cohen's kappa value of 0.88). In online experiments, the proposed method improves navigation performance in complex scenarios such as cornering and obstacle avoidance, yielding higher predicted competence and better navigation metrics than the default navigation baseline. In simpler scenarios, where the baseline already performs near-optimally, the causal adaptation provides limited benefit. These results indicate that causal models are particularly effective in enhancing navigation under increased task complexity. Overall, our results demonstrate that causal models developed for behavioural interpretation can be successfully integrated into real-robot navigation systems.

翻译：因果性在机器人学领域旨在通过使机器人能够预测其行为后果，从而产生更具可解释性和灵活性的机器人行为；然而，将因果模型部署到现有系统（例如导航）并在真实环境中运行的探索仍不充分。本文针对在真实机器人实验中迁移因果模型这一挑战性问题展开研究，聚焦于导航场景。我们从两个方面开展研究：（i）将因果模型作为离线评估模块，用于预测记录的机器人导航轨迹的有效性，并将其与量化导航性能相关联；（ii）将因果模型作为在线自适应模块，在默认导航器预测有效性较低时进行干预。我们在走廊巡逻的物理服务机器人上验证了所提方法。结果表明，预测的有效性（competence）与路径效率正相关，与路径异常（次优行为）负相关。模型预测与人工标注具有高度一致性（Cohen's kappa值为0.88）。在线实验中，所提方法在转弯和避障等复杂场景中提升了导航性能，相较于默认导航基线获得了更高的预测有效性和更优的导航指标。在基线已接近最优的简单场景中，因果自适应带来的收益有限。这些结果表明，因果模型在任务复杂度增加时对提升导航尤为有效。总体而言，我们的研究成果证明，为行为解释而开发的因果模型可成功集成到真实机器人导航系统中。