Coupled pairs do not necessarily interact

Previous studies that examined paired sensorimotor interaction suggested that rigidly coupled partners negotiate roles through the coupling force [1-3]. As a result, several human-robot interaction strategies have been developed with such explicit role distribution [4-6]. However, the evidence for role formation in human pairs is missing; to understand how rigidly coupled pairs negotiate roles through the coupling, we systematically examined rigidly coupled pairs who made point-to-point reaching movements. Our results reveal the consistency of the coupling force during the movement, from the very beginning of interaction. Do partners somehow negotiate the roles prior to interaction? A more likely explanation is that the coupling force is a by-product of two people who independently planned their reaching movements. We developed a computational model of two independent motion planners, which explains inter-pair coupling force variability. We demonstrate that the coupling force alone is an unreliable measure of interaction, and that coupled reaching is not a suitable task to examine sensorimotor interaction between humans. [1] Reed KB, Peshkin M (2008), IEEE Trans Haptics 1: 108-20. [2] Stefanov N, Peer A, Buss M (2009), Proc Worldhaptics 51-6. [3] van der Wel RPRD, Knoblich G & Sebanz N (2011), J Exp Psychol 37: 1420-31. [4] Evrard P, Kheddar A (2009), Proc Worldhaptics 45-50. [5] Oguz S, Kucukyilmaz A, Sezgin T, Basdogan C (2010), Proc Worldhaptics 371-8. [6] Mörtl A, Lawitzky M, Kucukyilmaz A, Sezgin M, Basdogan C, Kirche S (2012), Int J of Robotics Research 31(13): 1656-74

Haptic human-human interaction does not improve individual visuomotor adaptation

Haptic interaction between two humans, for example, parents physically supporting their child while it learns to keep balance on a bicycle, likely facilitates motor skill acquisition. Haptic human-human interaction has been shown to enhance individual motor improvement in a tracking task with a visuomotor rotation perturbation. These results are remarkable given that haptically assisting or guiding an individual rarely improves their motor improvement when the assistance is removed. We, therefore, replicated a study that reported benefits of haptic interaction between humans on individual motor improvement for tracking a target in a visuomotor rotation. Also, we tested the effect of more interaction time and stronger haptic coupling between the partners on individual performance improvement in the same task. We found no benefits of haptic interaction on individual motor improvement compared to individuals who practised the task alone, independent of interaction time or interaction strength. We also found no effect of the interaction partner's skill level on individual motor improvement

Drivers of partially automated vehicles are blamed for crashes that they cannot reasonably avoid

People seem to hold the human driver to be primarily responsible when their partially automated vehicle crashes, yet is this reasonable? While the driver is often required to immediately take over from the automation when it fails, placing such high expectations on the driver to remain vigilant in partially automated driving is unreasonable. Drivers show difficulties in taking over control when needed immediately, potentially resulting in dangerous situations. From a normative perspective, it would be reasonable to consider the impact of automation on the driver’s ability to take over control when attributing responsibility for a crash. We, therefore, analyzed whether the public indeed considers driver ability when attributing responsibility to the driver, the vehicle, and its manufacturer. Participants blamed the driver primarily, even though they recognized the driver’s decreased ability to avoid the crash. These results portend undesirable situations in which users of partially driving automation are the ones held responsible, which may be unreasonable due to the detrimental impact of driving automation on human drivers. Lastly, the outcome signals that public awareness of such human-factors issues with automated driving should be improved

Haptic human-human interaction: motor learning & haptic communication

Haptic interaction with a partner – interaction by exerting forces onto each other directly or through an object – plays an important role in our lives. It can help us to coordinate our actions and it can benefit learning of new motor tasks; for example, a therapist can physically support a patient during recovery of their motor functions after injury or disease. My research goal is to create a better understanding of whether haptic interaction between two humans improves individual motor learning and why haptic interaction would improve motor performance. We performed two experiments in which two partners learned novel motor tasks together: tracking a randomly-moving target in two novel environments. We haptically-connected the partners while they simultaneously learned a motor task. The partners were not made aware of the coupling. Although haptic interaction improved performance of both partners during interaction, this improvement was not retained when performing the task alone in both experiments. Hence, haptic interaction between humans does not improve individual motor learning in a collaborative motor task. Interestingly, we found that haptic interaction improved motor performance during interaction, even when being coupled to a worse-performing partner. To explain this result, we developed a computational model of the interaction in which we mechanically coupled two simulated partners who both independently performed the same motor task. The model assumed that the partners were unaware of the haptic connection. Hence, the simulated partners were only mechanically influenced by the interaction force; they did not exchange any information about each other or the task through the interaction force to improve their performance. This model accurately predicted the improvement due to interaction observed in the experimental data. Additional model analysis suggested that haptic interaction improved performance because the compliant connection partially compensated for each partner's motor output variability, which includes tracking errors such as overshoots. The worse-performing partners additionally benefited from the haptic guidance provided by their better-performing partners. Similarly, we observed that partners did not coordinate reaching movements through the interaction force in another experiment. In conclusion, our findings suggest that haptically-coupling two humans does not necessarily result in any exchange of information or motor coordination through the interaction force

Resistance is Not Futile: Haptic Damping Forces Mitigate Effects of Motor Noise during Reaching

Understanding how users adapt their motor behavior to damping forces can improve assistive haptic shared control strategies, for instance in heavy robot-assisted lifting applications. In previous experiments we showed that subjects reaching in constant and position-dependent longitudinal damping fields were able to reduce their movement time and increase end-point accuracy. The movement time versus movement distance and prescribed end-point accuracy agreed with Fitts' Law. However, why subjects were able to have shorter movement time while subjected to impeding damping forces is not explained by Fitts' Law. Based on the minimal variance principle we propose that humans exploit the noise-filtering behavior of constant or position-dependent damping forces. These damping forces attenuate mechanical effects of activation-dependent motor noise. This allows for higher motor activation and shorter movement time without losing end-point accuracy. Consequently, higher allowed motor activation allows for higher accelerations that lead to higher peak velocities, resulting in shorter movement times. Linear and non-linear stochastic optimal feedback control and optimal estimation models with multiplicative noise corroborate measurement data, supporting our hypothesis

Position and velocity priority in the motion plan explains endpoint bias.

<p>(A) The (decoupled) blue controller prioritises position, which causes it to overshoot the target. The (decoupled) red controller prioritises velocity such that it converges to the target without any overshoot. When the position-priority and velocity-priority controllers are coupled (dashed black trace), a force pattern is observed (dashed blue and red traces). The only manner in which the controllers would end the movement with constant torque is if they decide to hold their position once the reach is fulfilled. (B) Endpoint bias at the end of the reaching movement from all 16 partners. Partners, at movement onset, who pushed towards the target overshot it, and those who pulled away undershot it.</p

Dyadic reaching with initial opposing torque prior to movement.

<p>Torque from dyads I–IV in the coupled and both push-pull blocks; each bold trace is the average trajectory of each bin. In all dyadic reaching blocks, the torque was unchanging between trials within each block.</p

Simulation of dyadic reaching.

<p>Trajectories and torques from dyads I to IV (A–D) in the coupled and push-pull blocks. Solid trace is from the data showing the mean of all trials and the shaded area is the 95% confidence interval; the dashed traces are from simulations. First, we identify the state costs of both partners in coupled reaching, then identify the state cost in the push-pull blocks to see what effect the opposing torques prior to movement onset had. In all dyads, the initial opposing torque had a consistent effect: partners pushing towards the target prioritised position, and overshot the target; those pulling away prioritised velocity and undershoot the target.</p