Organizations: Faculty of Electrical Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden. · The University of Texas at Austin, Austin, Texas, USA. · Yale University, New Haven, Connecticut, USA.
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 & tables
Fig. 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).
Is the robot moving towards/away from the goal? (time-series)
competence
{0,1}t=8
Perceived competence at the end of an observation (categorical)
intention
{0,1}t=8
Perceived intention at the end of an observation (categorical)
TABLE I : CBN variables X . θtrobot_goal denotes the angle between robot and goal and distt denotes the Euclidean distance at time t .
Fig. 2 : Proposed CBN graph for the Robot-Following task.
Fig. 3 : Cluster means: robot_pos_change and robot_rotation_change.
Fig. 4 : Image series of a navigation-task video for our user study (Sec. VI ). 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 [ 28 ] 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.
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.
Zhitao Liang, Alex Mitrevski, Emmanuel Dean +1
Division for Systems and Control, Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
Deploying robots in human-shared environments requires deciding not only how to navigate, but also when dedicated social signals are worth emitting. Classic observation-based inference can be misleading in such settings: confounding factors may bias the estimated relationship between a signal and the task outcome, leading to incorrect decisions. Causal inference provides a framework for disentangling these relationships by modelling the mechanisms linking observations, actions, and outcomes, and by estimating interventional effects after accounting for confounding paths. We study a human-robot interaction scenario in a corridor and use a causal model to estimate whether a social navigation signal causally improves transit success. We represent the interaction as a causal DAG involving robot action, pedestrian occupancy, available space, task outcome, and a controlled confounder (static obstacles). We compare the interventional estimate with the observational estimate obtained by conditioning only on the robot action, which does not explicitly model interventions. Our results show that accounting for confounding variables can reverse the conclusions drawn from naive observational analysis: the observational estimate wrongly suggests that the social signal is detrimental, whereas the causal estimate shows that it is beneficial. This discrepancy highlights how failing to account for confounding can lead to incorrect conclusions and demonstrates the importance of causal models for context-aware signalling decisions in social robot navigation.
Francesco Baldo, Luca Castri, Gloria Beraldo +1
Department of Information Engineering, University of Padua · Institute of Cognitive Sciences and Technologies, National Research Council of Italy
Effective social robot navigation requires sensitivity to human behavior, often revealed through subtle skeletal cues like gait and orientation. We present Human-Aware Implicit Social Robot Navigation (HumAIN), a novel framework that fuses implicit social cues directly into the planning loop via knowledge distillation. We first employ a transformer-based teacher model that fuses rich multi-modal inputs, including historic images, skeletal keypoints, robot state, and a robot's target goal, to learn robust, human-aware representations for the robot's future trajectory planning. To enable real-time deployment, we then distill this knowledge into a lightweight student model. By optimizing for both trajectory reconstruction and latent feature alignment with the teacher, the student learns to infer complex social dynamics from minimal inputs. Bridging the prediction-planning gap with an efficient distilled architecture, our method enables robots to reason about human behavior in a manner that is adaptive, robust, and socially compliant. We validate HumAIN through extensive experiments, where it improves trajectory prediction metrics by an average of 29.8% across all metrics compared to state-of-the-art baselines. These results highlight the benefit of using implicit, whole-body cues to achieve human-like navigation awareness on resource-constrained platforms.
Daeun Song, Nhat Le, Jeffrey Chen +7
Ewha Womans University, Korea. · George Mason University, USA. · University of Virginia, USA. +3