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.

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.

Robot-assisted haptic training has the potential to facilitate motor learning and neurorehabilitation for a diverse number of motor tasks, ranging from manipulating objects with unknown dynamics to relearning walking using robotic exoskeletons. In this chapter, we provide an overview of current haptic methods evaluated in motor (re)learning studies with the goal to discuss implications for the design of rehabilitation technology. We highlight the challenge point framework as a unifying view on how to guide the design of haptic training paradigms, based on the initial skill level of the learner and the characteristics of the task to be learned. Future work on robot-aided haptic training strategies should focus on adaptive training algorithms, providing more naturalistic congruent multisensory feedback that resembles out-of-the-lab training, and conduct long-term studies to assess the efficacy of haptic training on learning not only the trained task but importantly, on skill transfer to real tasks.

We present a method for arbitration between human and robot involvement in a collaborative physical task execution based on ergonomic metrics. The existing methods for ergonomic control of physical human-robot collaboration perform the real-Time arbitration primarily based on a single type of ergonomic metric. The novelty of our approach is twofold. First, the system enables real-Time arbitration based on combining two types of ergonomic metrics: preventive and reactive. Second, we use a preventive metric to prevent worker fatigue and discomfort due to overexertion in the future and a reactive metric to avoid immediate fatigue and discomfort. To this end, we considered two metrics respectively: human arm manipulability and muscle fatigue. The developed multi-metric arbitration method translates the human multi-metric state to a robot control level over a collaborative task execution using a finite state machine. We demonstrate the proposed method on a Kuka LWR iiwa robotic arm in a collaborative human-robot polishing task that requires a specific force production.

Haptic interaction between two humans, for example, a physiotherapist assisting a patient regaining the ability to grasp a cup, likely facilitates motor skill acquisition. Haptic human–human interaction has been shown to enhance individual performance improvement in a tracking task with a visuomotor rotation perturbation. These results are remarkable given that haptically assisting or guiding an individual rarely benefits their individual improvement when the assistance is removed. We, therefore, replicated a study that reported that haptic interaction between humans was beneficial for individual improvement for tracking a target in a visuomotor rotation perturbation. In addition, we tested the effect of more interaction time and a stronger haptic coupling between the partners on individual improvement in the same task. We found no benefits of haptic interaction on individual improvement compared to individuals who practised the task alone, independent of interaction time or interaction strength.