In order to improve skill acquisition and neurorehabilitation, we need to improve our understanding of human motor learning. It has been shown that innate variability of movements made by an individual when performing a motor task (motor variability) might enhance skill acquisition. Augmenting motor variability could therefore be a promising method to enhance learning. However, current methods that enhance motor variability show divergent results and need to be better understood.
In a lab-based experiment with twenty healthy participants,
we studied the effect of a new method that haptically increases participants’ motor variability in learning a dynamic task, i.e., controlling a pendulum. This new method consisted of applying pseudo-random perturbation forces to the internal degree of freedom of the dynamic system (indirect haptic noise), instead of applying forces directly on the trainee’s hands as previously studied. The main task consisted of swinging a virtual pendulum to hit incoming targets with the pendulum ball. To assess generalization of learning we used two transfer tasks, which consisted of altered target positions or altered task dynamics (i.e., a pendulum with shorter rod length). We evaluated the effect of the new method
on learning by comparing performance gains after training to a control group who trained without perturbations. We found that the perturbations successfully increased participants’ motor variability during training. Although we observed no learning benefits of training with this indirect haptic noise for the trained
task compared to the control group, we observed divergent effects for transfer of learning. Participants that trained with indirect haptic noise seemed to benefit in transfer of learning to altered task dynamics but not in the task with altered target positions. Increasing motor variability by indirect haptic noise is promising for enhancing skill acquisition, specially in transfer of learning, and in tasks that incorporate complex dynamics. However, more research is needed to make indirect haptic noise a valuable tool for real life motor learning situations, e.g., in
robotic neurorehabilitation.

More and more vehicles have multiple advanced driver-assistance systems (ADAS), that take over tasks from the human driver, thereby taking the driver out of the loop of control. This might create a discrepancy between the responsibility that the human driver feels and the responsibility that is attributed to them when something goes wrong. Previous studies into perceived responsibility were mostly conducted in traded control systems, in which either the vehicle or the driver was performing the task, and tasks were shifted between them. In haptic shared control systems the automation and human driver cooperate continuously. The Level of Haptic Authority (LoHA) determines how strong the controller enforces its guidance. We examine how this LoHA impacts the driver's own perceived outcome responsibility, as well as that attributed to the automation when the automation makes a mistake. We found that when authority is shifted towards the car, the human driver feels less responsible, and attributes more responsibility to the automation, but only to a certain degree. Our findings correspond with previous research and with our own hypothesis. They add a new perspective to the current literature, as this is the first research-paper to examine responsibility perception in haptic shared driving from the drivers perspective. More research in the human driver's experience is needed to better understand human behaviour whilst driving with driving automation systems.

To make the cooperation within a physical human-robot team as efficient as possible, the team members must be able to co-adapt.
We developed and evaluated a robot that adapts to a human, using an adaptation strategy, in such a way as to guide the co-adaptation to have a positive effect on human task contribution and team performance.
A novel adaptive control algorithm for the robot was designed, estimating and adapting to the human control, using a Nash equilibrium to compute the robot's control inputs. Stability of the controller was theoretically proven, and validated using physical tests.
Two robot adaptation strategies, positive and negative reinforcement, were compared in an experiment in which 18 participants participated. The negative reinforcement adaptation strategy provides assistance on an assist-as-needed basis, whereas the positive reinforcement strategy is designed to intrinsically motivate humans to contribute to the control task.
Results show a significant increase in performance in the negative reinforcement adaptation strategy compared to the positive reinforcement adaptation strategy, whereas both conditions show a significant increase in performance compared to manual control. Results additionally show a significant decrease in both estimated (by the robot) and perceived (by the human) control share in the negative reinforcement adaptation strategy compared to the positive reinforcement adaptation strategy.
In conclusion, to guide the co-adaptation to both increase performance and engage humans to actively contribute to a control task, a robot should be designed to adapt using a positive adaptation strategy.

Human-robot interaction is a growing field that aims to research and develop communication channels between humans and robots to enhance comfort, safety, and productivity in healthcare, the household, and the industry. Researchers have considered ergonomy-related metrics to compose these channels for physical human-robot collaborative scenarios. We refer to these communication channels as arbitration methods. Several of these metrics, such as human arm manipulability and muscle fatigue, have taken their turns in the literature to set the base for arbitration methods reaching promising results. Human arm force manipulability represents the transmission between joint torques in the joint space and end-point force in the task space depending on the configuration of the joint angles. Muscle fatigue keeps track of the muscle activation and builds up depending on the muscle activation level and previous fatigue value. The first one has predictive value. The other has a reactive value.\par
Nevertheless, no work in the literature explores the power of combining both metrics into an arbitration method. Here we develop a multi-metric arbitration method that combines human arm force manipulability and muscle fatigue as input for a finite state machine (FSM) that translates the human multi-metric state to robot control level over a collaborative task. Although some modifications may be worth trying and evaluating to reach generalizability in physical human-robot collaborative tasks, the system reached satisfactory results. Moreover, as future steps, we should conduct human-factors research to compare the effect of the system on task performance.