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.

Space exploration is characterized by a limited amount of resources and tools. This particularly stands out in habitat construction, where heavy machinery like cranes are unavailable and manual work still plays a key role. To mitigate this, we propose a human-robot collaboration method for habitat construction tasks, which involve several key subtasks: grasping objects of various shapes, carrying them, and aligning them for assembly. The proposed method is based on an impedance controller and includes four modes of operation, that are tailored for specific sub-tasks. Each mode prescribes a robot stiffness behavior, needed for collaborative execution. The human operator can easily switch between the mode in realtime via a voice interface. To demonstrate the functionality of the proposed method in the construction task, we performed an experiment using KUKA LBR iiwa robot arm and qb robotics SoftHand robotic hand. These results indicate that the method offers a practical solution for human-robot collaborative construction tasks.