This study examines how fixed robot personalities (patient, impatient, leader, follower) influence co-learning in human-robot teams by answering the research question: How do different robot personalities influence co-learning. To do this, we implemented a reinforcement learning framework for a handover task where a robot and human participant co-learn to solve a task. The robot has personalities encoded along two axes: patient/impatient (via motion speed and stiffness) and leader/follower (via exploration rates and reward structures in phased Q-learning).

Through a within-subject design, we analyze policy metrics and human perceptions. While task success rates remain stable, strategy and internal policy metrics vary significantly. This underpins the key finding: robot personality does not affect task performance since humans can adapt to overcome subtle differences in robot personality. However, robot personality significantly affects how the collaboration is performed as human-robot teams adopt different strategies for different robot personalities.

Results demonstrate that robot personality is salient for differences in physical behaviour yet is unperceivable for modifications of internal parameters like exploration rate/decay and reward function for short interactions. This work bridges a critical gap in understanding how static robot traits shape collaborative adaptation, even when overt performance metrics remain unchanged.