A Causal Approach to Predicting and Improving Human Perceptions of Social Navigation Robots

Abstract

As mobile robots are increasingly deployed in human environments, enabling them to predict how people perceive them is critical for socially adaptable navigation. Predicting perceptions is challenging for two main reasons: (1) HRI prediction models must learn from limited data, and (2) the obtained models must be interpretable to enable safe and effective interactions. Interpretability is particularly important when a robot is perceived as incompetent (e.g., when the robot suddenly stops or rotates away from the goal), as it allows the robot to explain its reasoning and identify controllable factors to improve performance, requiring causal rather than associative reasoning. To address these challenges, we propose a Causal Bayesian Network designed to predict how people perceive a mobile robot's competence and how they interpret its intent during navigation. Additionally, we introduce a novel method to improve perceived robot competence employing a combinatorial search, guided by the proposed causal model, to identify better navigation behaviors. Our method enhances interpretability and generates counterfactual robot motions while achieving comparable or superior predictive performance to state-of-the-art methods, reaching an F1-score of 0.78 and 0.75 for competence and intention on a binary scale. To further assess our method's ability to improve the perceived robot competence, we conducted an online evaluation in which users rated robot behaviors on a 5-point Likert scale. Our method statistically significantly increased the perceived competence of low-competent robot behavior by 83%.

Figures (4)

• Figure 1: Example of low-competence robot behavior (a). Our causal model (b) predicts perceived competence by analyzing environmental cues such as the robot's trajectory. When low competence is predicted, the model identifies the minimal environment change (e.g., robot behavior) that is expected to lead to higher competence (c).
• Figure 2: Proposed CBN graph for the Robot-Following task.
• Figure 3: Cluster means: robot_pos_change and robot_rotation_change.
• Figure 4: Image series of a navigation-task video for our user study (Sec. \ref{['sec:study']}). The blue arrow represents the robot’s position and orientation, the red arrow depicts the follower, and other agents are shown as grey arrows. The goal, at the top of the images, is a green rectangle. Black areas indicate static obstacles, white areas are navigable space, and the surrounding grey region lies outside the 7.2m public space jensen18 where the 2D map was recorded. The upper series shows the original robot behavior, classified as low competence by our model, while the lower series depicts the counterfactual behavior generated to address this low-competence trajectory. Images are best viewed in color.