In this work, we investigate two ways to make robots proactive. Proactive robots are able to act on their own initiative in an anticipatory way to benefit humans. Robots sharing their space with humans need to be proactive in order to be helpful. Using a participatory design approach, as to align robot capabilities to end-users’ preferences, will enhance HRC and improve job quality. Design characteristics of HRC, such as collaboration design, robot design and workplace design affect job quality related factors. Practitioner Summary: The effect of human robot collaboration (HRC) on job quality is still under debate. Implementing predictable robots, that offer a clear advantage to the human and take into account operators’ preferences, will bring us closer to a human-centered collaboration. Increasing awareness and being able to adapt the robot to the individual operator are crucial to improve the aforementioned factors. We identified four job quality related factors that are of relevance in HRC: (1) Cognitive Workload, (2) Collaboration Fluency, (3) Trust, and (4) Acceptance and Satisfaction. How does HRC influence job quality on an individual level and how can we adapt HRC to boost positivity at work? Although there are different hypotheses on potential effects of HRC on job quality, defined as the quality of the working environment and its effect on the employee’s well-being, a comprehensive theory is still lacking. In this review we address the human in human robot collaboration (HRC). This shows that asking for the robot’s help may lead to a more “social” interaction, without improving the quality of interaction or the system performance. We also found that participants gazed at the robot’s face more during the human-initiated help compared to the other conditions. However, they prefer having control of when the robot should help, rather than working with a reactive robot that only helps when needed. We found that people collaborate best with a proactive robot, yielding better team fluency and high subjective ratings. We performed a user study (N=18) to compare the trigger mechanisms in terms of quality of interaction, system performance and perceived sociality of the robot. Human-initiated help gives control of the robot action timing to the user robot-initiated reactive help triggers robot assistance when it detects that the human needs help robot-initiated proactive help makes the robot help whenever it can. To evaluate our system, we implemented three different initiative conditions to trigger the robot’s actions.
Our system is able to predict future environmental states and the robot’s actions to reach them using a dynamic Bayesian network. We designed a robotic system capable of autonomously performing table-top manipulation tasks while monitoring the environmental state. Here, we address the question of whether and when a robot should take initiative during joint human-robot task execution.
The promise of robots assisting humans in everyday tasks has led to a variety of research questions and challenges in human-robot collaboration.
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