arXiv subjects

David A. Abbink

Publications and source records attributed to David A. Abbink.

6 recordsLinked to original sources

arXiv2026

Feasible Action Space Reduction for Quantifying Causal Responsibility in Continuous Spatial Interactions

Understanding the causal influence of one agent on another agent is crucial for safely deploying artificially intelligent systems such as automated vehicles and mobile robots into human-inhabited environments. Existing models of causal responsibility deal with simplified abstractions of scenarios with discrete actions, thus, limiting real-world use when understanding responsibility in spatial interactions. Based on the assumption that spatially interacting agents are embedded in a scene and must follow an action at each instant, Feasible Action-Space Reduction (FeAR) was proposed as a metric for causal responsibility in a grid-world setting with discrete actions.Since real-world interactions involve continuous action spaces, this paper proposes a formulation of the FeAR metric for measuring causal responsibility in space-continuous interactions. We illustrate the utility of the metric in prototypical space-sharing conflicts, and showcase its applications for analysing backward-looking responsibility and in estimating forward-looking responsibility to guide agent decision making. Our results highlight the potential of the FeAR metric for designing and engineering artificial agents, as well as for assessing the responsibility of agents around humans.

cs.MA↗

arXiv2026

Using Feasible Action-Space Reduction by Groups to fill Causal Responsibility Gaps in Spatial Interactions

Heralding the advent of autonomous vehicles and mobile robots that interact with humans, responsibility in spatial interaction is burgeoning as a research topic. Even though metrics of responsibility tailored to spatial interactions have been proposed, they are mostly focused on the responsibility of individual agents. Metrics of causal responsibility focusing on individuals fail in cases of causal overdeterminism - when many actors simultaneously cause an outcome. To fill the gaps in causal responsibility left by individual-focused metrics, we formulate a metric for the causal responsibility of groups. To identify assertive agents that are causally responsible for the trajectory of an affected agent, we further formalise the types of assertive influences and propose a tiering algorithm for systematically identifying assertive agents. Finally, we use scenario-based simulations to illustrate the benefits of considering groups and how the emergence of group effects vary with interaction dynamics and the proximity of agents.

cs.MA↗

arXiv2026

Linking Behaviour and Perception to Evaluate Meaningful Human Control over Partially Automated Driving

Partial driving automation creates a tension: drivers remain legally responsible for vehicle behaviour, yet their active control is significantly reduced. This reduction undermines the engagement and sense of agency needed to intervene safely. Meaningful human control (MHC) has been proposed as a normative framework to address this tension. However, empirical methods for evaluating whether existing systems actually provide MHC remain underdeveloped. In this study, we investigated the extent to which drivers experience MHC when interacting with partially automated driving systems. Twenty-four drivers completed a simulator study involving silent automation failures under two modes - haptic shared control (HSC) and traded control (TC). We derived behavioural metrics from telemetry data, subjective perception scores from post-trial surveys and used them to test hypothesised relations between them derived from the properties of systems under MHC. The confirmatory analysis showed a significant negative correlation between the perception of the automated vehicle (AV) understanding the driver and conflict in steering torques. An exploratory analysis also revealed a surprising positive correlation between reaction times and the perception of sufficient control. Qualitative feedback from open-ended post-experiment questionnaires revealed that mismatches in intentions between the driver and automation, lack of safety, and resistance to driver inputs contribute to the reduction of perceived MHC, while subtle haptic guidance aligned with driver intent had a positive effect. These findings suggest that future designs should prioritise effortless driver interventions, transparent communication of automation intent, and context-sensitive authority allocation to strengthen meaningful human control in partially automated driving.

cs.HC↗

arXiv2025

Fitts' List Revisited: An Empirical Study on Function Allocation in a Two-Agent Physical Human-Robot Collaborative Position/Force Task

In this letter, we investigate whether classical function allocation-the principle of assigning tasks to either a human or a machine-holds for physical Human-Robot Collaboration, which is important for providing insights for Industry 5.0 to guide how to best augment rather than replace workers. This study empirically tests the applicability of Fitts' List within physical Human-Robot Collaboration, by conducting a user study (N=26, within-subject design) to evaluate four distinct allocations of position/force control between human and robot in an abstract blending task. We hypothesize that the function in which humans control the position achieves better performance and receives higher user ratings. When allocating position control to the human and force control to the robot, compared to the opposite case, we observed a significant improvement in preventing overblending. This was also perceived better in terms of physical demand and overall system acceptance, while participants experienced greater autonomy, more engagement and less frustration. An interesting insight was that the supervisory role (when the robot controls both position and force) was rated second best in terms of subjective acceptance. Another surprising insight was that if position control was delegated to the robot, the participants perceived much lower autonomy than when the force control was delegated to the robot. These findings empirically support applying Fitts' principles to static function allocation for physical collaboration, while also revealing important nuanced user experience trade-offs, particularly regarding perceived autonomy when delegating position control.

cs.RO↗

arXiv2025

Achieving Meaningful Collaboration: Worker-centered Design of a Physical Human-Robot Collaborative Blending Task

The use of robots in industrial settings continues to grow, driven by the need to address complex societal challenges such as labor shortages, aging populations, and ever-increasing production demands. In this abstract, we advocate for (and demonstrate) a transdisciplinary approach when considering robotics in the workplace. Transdisciplinarity emphasizes the integration of academic research with pragmatic expertise and embodied experiential knowledge, that prioritize values such as worker wellbeing and job attractiveness. In the following, we describe an ongoing multi-pronged effort to explore the potential of collaborative robots in the context of airplane engine repair and maintenance operations.

cs.RO↗

arXiv2023

Merging in a Coupled Driving Simulator: How do drivers resolve conflicts?

Traffic interactions between merging and highway vehicles are a major topic of research, yielding many empirical studies and models of driver behaviour. Most of these studies on merging use naturalistic data. Although this provides insight into human gap acceptance and traffic flow effects, it obscures the operational inputs of interacting drivers. Besides that, researchers have no control over the vehicle kinematics (i.e., positions and velocities) at the start of the interactions. Therefore the relationship between initial kinematics and the outcome of the interaction is difficult to investigate. To address these gaps, we conducted an experiment in a coupled driving simulator with a simplified, top-down view, merging scenario with two vehicles. We found that kinematics can explain the outcome (i.e., which driver merges first) and the duration of the merging conflict. Furthermore, our results show that drivers use key decision moments combined with constant acceleration inputs (intermittent piecewise-constant control) during merging. This indicates that they do not continuously optimize their expected utility. Therefore, these results advocate the development of interaction models based on intermittent piecewise-constant control. We hope our work can contribute to this development and to the fundamental knowledge of interactive driver behaviour.

cs.HC↗