The interplay between haptic guidance and personality traits in robotic-assisted motor learning
Alberto Garzás-Villar (TU Delft - Human-Robot Interaction, Erasmus Universiteit Rotterdam)
Caspar Boersma (Student TU Delft)
Alexis Derumigny (TU Delft - Statistics)
J. Micah Prendergast (TU Delft - Human-Robot Interaction)
Arkady Zgonnikov (TU Delft - Human-Robot Interaction)
Jane Murray Cramm ( Erasmus Universiteit Rotterdam)
Laura Marchal-Crespo (Erasmus MC, TU Delft - Human-Robot Interaction)
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Abstract
Background: Robotic devices have shown promise in supporting motor (re)learning. However, there is a limited understanding of how personality traits influence the effectiveness of robot-aided training strategies. Methods: We conducted a motor learning experiment with 40 unimpaired participants who trained to control a virtual pendulum using a robotic haptic device. Before the experiment, we assessed personality traits including the perceived control over life events (Locus of Control), the tendency to turn challenges into engaging activities (Transform of Challenge), and other subscales from Autotelic and Hexad gaming style questionnaires. Participants were divided into two groups, one receiving haptic guidance during training and a second one without assistance. Short- and long-term retention was assessed, and relationships between personality traits, performance metrics, and human-robot interaction metrics were analyzed. Results: Participants with high Transform of Challenge or external Locus of Control characteristics who received physical guidance during training reduced the human-robot interaction forces to a lesser extent compared to the ones who did not receive guidance. Additionally, participants with a high Free Spirit gaming style showed greater sensitivity to how their perception of the guidance affected their performance during the retention phases. Conclusion: Our findings suggest that autotelic personality, Locus of Control, and gaming style modulate motor learning outcomes during robotic-assisted training, affecting both performance and human-robot interaction metrics. This highlights the potential of integrating personality-based adaptations in robot-aided rehabilitation protocols to enhance performance and motor (re)learning. Future works should explore the relationship between personality traits and psychological states (e.g., perceived difficulty, attention) across diverse tasks and guidance methods in clinical populations.
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