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.

Force perturbations during gait training can increase movement variability, which may support motor exploration and learning. However, when such perturbations are delivered through a robotic exoskeleton, they can also reduce perceived exoskeleton transparency, potentially hindering user acceptance. We tested whether visualizing continuous upper leg-level perturbations in immersive virtual reality (VR) could preserve their variability-enhancing effect while mitigating the cost to perceived transparency in a pelvis-centered walking task. Thirty healthy adults walked on a treadmill while wearing a robotic exoskeleton and performed a ball-in-cup task, requiring continuous mediolateral control of the pelvis. Participants trained under one of three conditions: Control (no perturbations), Perturbation (continuous noise-like multisine forces applied at the thighs), or Perturbation + Visual (same forces with a real-time, body-referenced force-beam visualization). Step-width variability was evaluated during Training. Task performance, intrinsic motivation, and perceived transparency were assessed across Baseline, Training, Retention, and a faster-speed Transfer test (120% of preferred speed). Both perturbation conditions significantly increased step-width variability during Training relative to Control, with no detectable difference between Perturbation and Perturbation + Visual. Task performance improved from Baseline to Retention and Transfer across all conditions, with no significant differences across conditions. Motivation did not differ between conditions either. Critically, perceived transparency decreased only in the non-visualized Perturbation condition and remained stable in both Control and Perturbation + Visual. Our results show that continuous leg-level perturbations reliably enrich lateral gait variability and that simple visual force cues can prevent a perceived transparency cost without compromising the variability manipulation. Future work should test adaptive dosing, multi-session training, and clinical cohorts with impaired lateral stability.

Gait variability, the subtle fluctuations in walking patterns, is crucial for adaptation and motor learning. While existing methods to increase gait variability often rely on force-based perturbations, these can reduce motivation. This study explored if a subtle visual feedback distortion (VFD), applied to a first-person avatar's foot position in an immersive virtual reality environment, could increase gait variability without such a drawback. Twenty healthy adults walked on a treadmill wearing a head-mounted display and motion trackers, performing a stepping task under two conditions: with and without VFD. The VFD introduced a continuously changing, noise-like offset to the displayed foot positions, designed to be minimally noticeable. We quantified gait variability through the standard deviation of step width and step length and collected self-report measures on embodiment, motivation, and simulator sickness. We found that VFD significantly increased step width variability by about 15%, indicating enhanced lateral adaptability. In contrast, step length variability remained unchanged. Participants adjusted their foot placement in the opposite direction of the visual distortion, supporting the idea that proprioceptive recalibration underpinned the observed changes. Notably, this increase in variability occurred without any significant effects on embodiment, motivation, or simulator sickness. These findings suggest that subtle VFD can enhance gait variability - potentially facilitating motor learning and adaptability - while preserving user experience and motivation. Future research should determine whether such VFD-based interventions yield lasting functional improvements and investigate their applicability in rehabilitation contexts, potentially offering a noninvasive, user-friendly approach to promoting healthy gait dynamics.

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 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.

Abstract: 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. (Figure presented.). 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.

Combining biomechanical modeling with robotic physiotherapy is a promising direction to provide real-time insights during the rehabilitation of patients with musculoskeletal injuries, such as rotator-cuff tears. One aspect is to prevent re-injuries caused by high strain in the injured tissues while allowing patients to perform the required rehabilitation exercises. In this paper, we propose a novel shared control method for robots to limit unsafe patient movements, through physical guidance based on a strain-space representation of the human rotator cuff. The method provides motion corrections through two complementary predictive modules. The first module exerts a lower degree of intervention and is analogous to rumble strips or speed bumps for cars on the road. In this case, an impedance controller induces variable damping to slow down the patient's movement when a danger zone is approached. The second module produces a higher degree of intervention and is analogous to lane-assist in cars. In this case, the robot plans an optimal deflection trajectory and temporarily takes over control of the movement to avoid an unsafe situation. We performed experiments with a healthy participant acting as a patient and evaluated the effect of different human-robot interaction modalities on the resulting human movement in terms of avoidance of high-strain areas of the rotator-cuff tendons and contact forces exchanged.

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.

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.

Safe and socially acceptable interactions with human-driven vehicles are a major challenge in automated driving. A good understanding of the underlying principles of such traffic interactions could help address this challenge. Particularly, accurate driver models could be used to inform automated vehicles in interactions. These interactions entail complex dynamic joint behaviors composed of individual driver contributions in terms of high-level decisions, safety margins, and low-level control inputs. Existing driver models typically focus on one of these aspects, limiting our understanding of the underlying principles of traffic interactions. Here, we present a Communication-Enabled Interaction model based on risk perception, that does not assume humans are rational and explicitly accounts for communication between drivers. Our model can explain and reproduce observed human interactions in a simplified merging scenario on all three levels. Thereby improving our understanding of the underlying mechanisms of human traffic interactions and posing a step towards interaction-aware automated driving.

Traffic jams occurring on highways cause increased travel time as well as increased fuel consumption and collisions. So-called phantom traffic jams are traffic jams that do not have a clear cause, such as a merging on-ramp or an accident. Phantom traffic jams make up 50% of all traffic jams and result from instabilities in the traffic flow that are caused by human driving behavior. Automating the longitudinal vehicle motion of only 5% of all cars in the flow can dissipate phantom traffic jams. However, driving automation introduces safety issues when human drivers need to take over the control from the automation. We investigated whether phantom traffic jams can be dissolved using haptic shared control. This keeps humans in the loop and thus bypasses the problem of humans’ limited capacity to take over control, while benefiting from most advantages of automation. In an experiment with 24 participants in a driving simulator, we tested the effect of haptic shared control on the dynamics of traffic flow and compared it with manual control and full automation. We also investigated the effect of two control types on participants’ behavior during simulated silent automation failures. Results show that haptic shared control can help dissipating phantom traffic jams better than fully manual control but worse than full automation. We also found that haptic shared control reduces the occurrence of unsafe situations caused by silent automation failures compared to full automation. Our results suggest that haptic shared control can dissipate phantom traffic jams while preventing safety risks associated with full automation.

Research on motor learning has found evidence that learning rate is positively correlated with the learner's motor variability. However, it is still unclear how to robotically promote that variability without compromising the learner's sense of agency and motivation, which are crucial for motor learning. We propose a novel method to enhance motor variability during learning of a dynamic task by applying pseudo-random perturbing forces to the internal degree of freedom of the dynamic system rather than directly applying the forces to the learner's limb. Twenty healthy participants practiced swinging a virtual pendulum to hit oncoming targets, either with the novel method or without disturbances, to evaluate the effect of the method on motor learning, skill transfer, motivation, and agency. We evaluated skill transfer using two tasks, changing either the target locations or the task dynamics by shortening the pendulum rod. The indirect haptic disturbance method successfully increased participants' motor variability during training compared to training without disturbance. Although we did not observe group-level differences in learning, we observed divergent effects on skill generalization. The indirect haptic disturbances seemed to promote skill transfer to the altered task dynamics but limited transfer in the task with altered target positions. Motivation was not affected by the haptic disturbances, but future work is needed to determine if indirect haptic noise negatively impacts sense of agency. Increasing motor variability by indirect haptic disturbance is promising for enhancing skill transfer in tasks that incorporate complex dynamics. However, more research is needed to make indirect haptic disturbance a valuable tool for real-life motor learning situations.

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 letter 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 letter demonstrate how human-robot co-learning can be enabled in physically embodied tasks.