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.

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.

Despite a large body of research on robot learning, it has not yet been thoroughly studied how collaborating humans and robots learn reciprocally. In such situations, both humans and robots continuously learn about each other and the task through interaction. This paper addresses the research question: “How can human-robot co-learning be facilitated in physically embodied collaborative tasks?”. First, we derived five requirements for successful human-robot co-learning from literature: shared goal, synchrony, interdependence, adaptability, and transparency. Based on these requirements, we designed a collaborative human-robot handover task and a robot Q-learning method. In an evaluation with six human participants co-learning was indeed found to emerge in the hand-over task. Particularly, for three of the human-robot dyads, our designed setup proved to facilitate co-learning in a way that met all five requirements. The task and robot learning method presented in this paper demonstrate how human-robot co-learning can be enabled in physically embodied tasks.

Maintenance is a crucial factor in the life-critical domain of aviation. This heavy, physically demanding work can suffer from a growing labor shortage. To reduce physical load on technicians, we introduce two fan blade blending support systems utilizing pneumatic belt grinders as opposed to the manual blending with a hand file. The robotic support system comprises a collaborative manipulator operating in impedance control mode to hold the grinder, allowing physical interaction with the operator. The mechanical support system features an adjustable linear guide that constrains the motion of the grinder. To gain initial insights about the usability of the proposed two support systems, we examine them in the operational environment with actual expert workers. Results indicate a reduction in physical demand for the operation compared to the current manual operation of blending with a hand file. The mechanical support system was evaluated marginally higher compared to the robotic one, likely attributed to its operational simplicity. The economic feasibility of it opens the possibility for further adoption on the shopfloor, while the robotic system serves as a basis for additional exploration.

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.

Advances in haptic technologies enable rich, multi-channel haptic rendering of interactions with virtual objects during virtual reality training. However, it remains an open question whether multi-channel haptic rendering (kinesthetic and tactile) provides superior motor learning and transfer when training dynamic tasks compared to simpler, single-channel sensory information. We investigated how 40 participants learned to invert and balance a virtual pendulum after training under four haptic rendering conditions: congruent kinesthetic and tactile rendering, kinesthetic rendering alone, tactile rendering alone, and no haptic rendering. Kinesthetic information was delivered through a delta robot, and tactile information through a two-dimensional skin-stretch device at the finger pads. Participant performance was measured in catch trials during training, in short- and long-term retention trials, and with a transfer task with a shorter pendulum. Participants from all four training conditions demonstrated the ability to improve and transfer their skills. However, we observed poorer performance during catch trials when training with reduced or absent haptic rendering compared to training with congruent kinesthetic and tactile rendering. The advantage of congruent haptic rendering over conditions lacking kinesthetic rendering was maintained during short-term retention, whereas no significant performance differences were observed between conditions in long-term retention and the transfer task. These results suggest that congruent haptic rendering benefits the task’s early learning by supporting the generation of internal models of the task dynamics, with kinesthetic rendering playing a major role. Overall, our findings highlight the potential benefits of multi-channel haptic rendering to accelerate virtual reality training.

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.

This paper presents a multi-modal dynamic workspace re-indexing method for addressing operator ergonomics and workspace limitations. The proposed method has two interactive modes: pose-to-pose mode, which is active when the operator is within an ergonomic workspace of comfortable arm postures, and ergonomic workspace drift mode, which activates after the operator makes an excursion beyond the boundaries of the ergonomic workspace when trying to reach more distant targets with the remote robot. In the ergonomic workspace drift mode, the operator temporarily stays slightly outside these boundaries, while the offset between the local and remote workspace drifts with a velocity proportional to the excursion distance. This dynamically re-indexes the remote workspace toward the distant target, and the operator can remain in a comfortable posture while the remote robot moves toward the intended target where the task is. To construct the ergonomic workspace, we employed the Rapid Upper Limb Assessment method. To validate the proposed method, we conducted experiments on a teleoperation setup involving a Force Dimension Sigma7 haptic device controlling a Kuka LBR iiwa robotic arm. The results show that the proposed controller successfully addresses workspace limitations by dynamically reindexing the follower's workspace towards target objects, while maintaining good operator ergonomics.

Touchscreens and touchpads offer intuitive interfaces but provide limited tactile feedback, usually just mechanical vibrations. These devices lack continuous feedback to guide users’ fingers toward specific directions. Recent innovations in surface haptic devices, however, leverage ultrasonic traveling waves to create active lateral forces on a bare fingertip. This paper investigates the effects and design possibilities of active forces feedback in touch interactions by rendering artificial potential fields on a touchpad. Three user studies revealed that: (1) users perceived attractive and repulsive fields as bumps and holes with similar detection thresholds; (2) step-wise force fields improved targeting by 22.9% compared to friction-only methods; and (3) active force fields effectively communicated directional cues to the users. Several applications were tested, with user feedback favoring this approach for its enhanced tactile experience, added enjoyment, realism, and ease of use.

This full-day workshop is dedicated to mapping and building a community toward shaping sustainable futures of robot-assisted work, with and for workers. The program encompasses lightning keynotes from HRI experts, talks from selected participants bringing case studies, and a hands-on mapping activity for understanding the landscape of robotics innovation at work - in order to foster knowledge exchange and learning. The workshop culminates in a facilitated conversation with workers and managers from a particular case study, providing real-world insights into the challenges, complexities and opportunities for our HRI community in shaping the future of work. With this rich set of activities, the program aims to bridge the gap between academics and practitioners, and to promote a holistic understanding of our roles in shaping the future of work, with particular attention to systemic challenges and value-driven approaches. The workshop will conclude with a synthesis of key takeaways and potential directions for future research and community building.

Despite recent advancements in physical humanrobot collaboration, measuring and distinguishing between forces applied by humans and robots remains challenging, limiting our understanding of force dynamics during collaboration. Our proposed solution addresses this gap with a low-cost, lightweight design that integrates directly at the robot endeffector level. The interface employs a three-ring mechanical structure with strategically positioned load cells and a Sarrus mechanism to constrain movement to the $\mathbf{z}$-axis only, enabling tool mounting for real-world collaborative tasks such as blending or sanding operations. Validation experiments demonstrate excellent force decoupling capabilities with minimal crossinterference, achieving Weighted Root Mean Squared Errors of 0.14 N for robot-applied forces and 0.08 N for human-applied forces compared to ground truth measurements in steadystate for loads ranging from 0 N up to 23 N. The Maximum Absolute Error in these experiments is 0.33 N, confirming high measurement accuracy. This affordable and integrated solution lowers the threshold for employing decoupled force sensing in collaborative tasks, making it more accessible for investigating force dynamics and developing adaptive control strategies in both research and practical applications of physical humanrobot collaboration.

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.

Understanding behavior of human drivers in interactions with automated vehicles (AV) can aid the development of future AVs. Existing investigations of such behavior have predominantly focused on situations in which an AV a priori needs to take action because the human has the right of way. However, future AVs might need to proactively manage interactions even if they have the right of way over humans, e.g., a human driver taking a left turn in front of the approaching AV. Yet it remains unclear how AVs could behave in such interactions and how humans would react to them. To address this issue, here we investigated behavior of human drivers (N = 19) when interacting with an oncoming AV during unprotected left turns in a driving simulator experiment. We measured the outcomes (Go or Stay) and timing of participants’ decisions when interacting with an AV which performed subtle longitudinal nudging maneuvers, e.g. briefly decelerating and then accelerating back to its original speed. We found that participants’ behavior was sensitive to deceleration nudges but not acceleration nudges. We compared the obtained data to predictions of several variants of a drift-diffusion model of human decision making. The most parsimonious model that captured the data hypothesized noisy integration of dynamic information on time-to-arrival and distance to a fixed decision boundary, with an initial accumulation bias towards the Go decision. Our model not only accounts for the observed behavior but can also flexibly generate predictions of human responses to arbitrary longitudinal AV maneuvers, and can be used for both informing future studies of human behavior and incorporating insights from such studies into computational frameworks for AV interaction planning.

INTRODUCTION:Cars are increasingly computerized, and vehicle settings such as steering gain (SG) can now be altered during driving. However, it is unknown whether transitions in SG should be adaptable (i.e., triggered by driver input) or adaptive (i.e., triggered automatically). We examined this question for road segments expected to require different SG. OBJECTIVE:This paper aimed to investigate whether SG mode changes should be made by the driver or automatically. METHODS:Twenty-four participants drove under four conditions in a simulator: fixed low gain (FL), fixed high gain (FH), a machine-initiated steering system, which switched between the two SG levels at predetermined locations (MI), and a driver-initiated steering system, in which the SG level could be changed by pressing a button on the steering wheel (DI). RESULTS:Participants showed poorer lane-keeping and reported higher effort for FH compared to FL on straights, while the opposite held true on curved roads. On curved roads, the MI condition yielded better lane-keeping and lower subjective effort than the DI condition. However, a substantial portion of the drivers gave low preference rankings to the MI system. CONCLUSION:Drivers prefer and benefit from a steering system with a variable rather than fixed gain. Furthermore, although automatic SG transitions reduce effort, some drivers reject this concept. APPLICATION:As the state of technology advances, MI transitions are becoming increasingly feasible, but whether drivers would want to delegate their decision-making authority to a machine remains a moot point.

Teleoperation is a crucial technology enabling human operators to control robots remotely to perform tasks in hazardous and difficult-to-access environments. Tasks in such environments often involve complex physical interactions with tools and objects of various softness. To this end, teleimpedance enables the operators to adjust the robot impedance in real-time to simplify such interactions. While the existing teleimpedance approaches provide several interfaces to command the robot impedance, there are no interfaces to visualize both the commanded impedance and that of the objects to be interacted with. This paper presents a novel interface to provide visual feedback on the impedance of remote robots and objects. To do so, we use virtual stiffness ellipsoids and different modes that display the individual impedance of the robot and objects as well as combined post-contact impedance. The key advantage of visual feedback on the impedance compared to force feedback is that the operator can see the interaction characteristics before the contact occurs. This enables the operator to act proactively before contact rather than just reactively after the contact. This paper also proposes a new intuitive way to command the robot impedance using mixed reality, interacting with these ellipsoids and modifying them as needed. To demonstrate the key functionalities of the developed interface, we performed proof-of-concept experiments on teleoperated tasks.

A task as simple as holding a cup between your fingers generates complex motor commands to finely regulate the forces applied by muscles. These fine force adjustments ensure the stability and integrity of the object by preventing it from slipping out of grip during manipulation and by reacting to perturbations. To do so, our sensorimotor system constantly monitors tactile and proprioceptive information about the force object exerts on fingertips and the friction of the surfaces to determine the optimal grip force. While the literature describes the transient responses, humans can generate to react to perturbations in load force, it is yet to be determined if humans can also react to abrupt changes in friction while already holding an object. Only recently technology using imperceivable ultrasonic vibrations became available to modulate friction in real time to investigate this question. In this study, we used an object with an integrated friction modulation device suspended in a pulley system controlling the load. With this device, we explored the rapid adaptation of the sensorimotor system to changes in friction alone and in combination with changes in load. When load force and friction changed simultaneously, the grip force response was regulated based on the grip safety requirements. Participants increased their grip force in response to decrease in friction. However, they did not adjust their grip force when the friction increased, which is expected based on our biomechanical model of friction sensing mechanisms. KEY POINTS: Simple tasks like pouring water into a glass mobilize intricate interactions between fingertip sensory inputs and motor commands to account for the weight change and friction. It has been investigated how humans react to force perturbations when holding an object, but very little is known about how frictional changes are sensed and acted upon while holding an object, for example, due to sweating or condensation. We engineered a unique experimental object that utilizes imperceivable ultrasonic vibrations to change the frictional properties of the surface in a few milliseconds. This apparatus enabled us to study how human subjects react to change of friction when gripping or holding an object. We showed that humans adjust the strength of their grasp when forces in the direction of gravity either increase or decrease; however, frictional change evokes adjustments only when friction decreases.

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 optimise 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