| dc.description.abstract | This thesis delves into a central challenge in human-robot collaboration (HRC): the adaptive task planning of robots to enhance team performance, fluency, and the human agent's perception of both the robot and the collaboration. This thesis tackles the challenge of proactive task planning and allocation in collaborative scenarios, involving a single human and a single robot working together to accomplish a task. Recognizing the existing gaps in the literature, our focus revolves around balancing human agents' leading/following preferences and their performance, with the aim of fostering collaboration while maintaining a high level of human perception of the robot.
After an in-depth review of related work, we initiate our exploration with an online user study, in a simulation environment using a manipulator robot. This study is designed to evaluate the impact of the robot's planning strategy on participants' perception of the robot and collaboration. This study incorporates three distinct planning strategies: prioritizing the human's objectives, prioritizing the robot's objectives, and achieving a balance between both agents' objectives. The results guide our assessment of how the balancing strategy, in particular, can uphold both team performance and a high level of participants' perception of collaboration and the robot, in comparison to the other strategies. However, a limitation arises as the study employs fixed strategies, randomly assigned to participants, irrespective of their preference and performance.
Building upon the results of the first user study, we address the limitations identified in the initial study by enabling the robot to estimate the human agent's leading/following preference. However, the human agent's preference is not the sole factor influencing the robot's decision-making process; the human agent's performance is also crucial for adjusting the team's overall performance, particularly in cases of the human agent's poor or suboptimal performance. Consequently, the robot also estimates the human agent's performance. Furthermore, the robot needs to be capable of updating the task state based on both agents' actions and mistakes. With an updated understanding of the human agent's performance, leading/following preference, and task state, the robot updates its plan for task allocation and scheduling to minimize collaboration costs.
Next, we evaluate the adaptability of the task planning framework and algorithm in a simulation environment, demonstrating its effectiveness across various human performance and preference scenarios. Yet, recognizing the unique challenges posed by human participants, the complete evaluation of the algorithm's effectiveness requires real-world scenarios, considering uncertainties inherent in human behavior and decision-making.
Subsequently, we tackle the challenges of implementing the task planning framework on a real robot, a mobile manipulator robot, within a carefully designed collaborative scenario. Providing details on the experimental setup and methodology, a system evaluation study highlights the robot's ability to adapt based on human behavior. Finally, we conduct a user study involving 48 participants, evaluating results from multiple perspectives, including participants' perception of the robot, tasks, and collaboration, participants' actions and performance, and the robot's actions and performance. Results from the study affirm the success of the task planning framework in achieving its objectives: enhancing team fluency by considering the human agents' preferences and performance while maintaining a high level of participants' perception of the robot and the human-robot collaboration.
This thesis also explores participants' leading/following preferences in collaboration, revealing a dominant preference to lead the robot. This finding can assist robotics and autonomous systems designers in considering this factor in their designs. Additionally, we evaluated the influence of participants' leadership and followership styles on their collaboration, warranting further and more in-depth future studies.
In summary, this thesis contributes a proactive task planning framework that takes into account both human leading/following preferences and performance, signifying an advancement in the field of human-robot collaboration. The validation through user studies offers valuable insights, laying the groundwork for future research and applications in the continually evolving domain of human-robot collaboration. | en |
