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.

This paper addresses the research question: “How can a human-robot team achieve co-learning, and interdependence in physically embodied tasks?”
A method has been developed that enables a human-robot team to co-learn the handover of an object from the robot to the human. Five design requirements were composed to address the challenges of human-robot co-learning in physically embodied environments. The method is based on a Q-learning algorithm that was adapted and extended to meet these requirements. An experiment was conducted with six participants. For every human-robot team, each design requirement was qualitatively evaluated. Interdependent co-learning was identified in three of the six teams. The limitation of the design, and how this method can be improved further, was discussed. The method, presented in this paper, demonstrates how human-robot co-learning and interdependence can be enabled in physically embodied tasks.