What is it about?
In this study, a novel time-driven mathematical model for trust is developed considering human-multi-robot performance for a Human-robot Collaboration (HRC) framework. For this purpose, a model is developed to quantify human performance considering the effects of physical and cognitive constraints and factors such as muscle fatigue and recovery, muscle isometric force, human (cognitive and physical) workload and workloads due to the robots' mistakes, and task complexity. The performance of multi-robot in the HRC setting is modeled based upon the rate of task assignment and completion as well as the mistake probabilities of the individual robots. The human trust in HRC setting with single and multiple robots are modeled over different operation regions, namely unpredictable region, predictable region, dependable region, and faithful region. The relative performance difference between the human operator and the robot is used to analyze the effect on the human operator's trust in robots' operation. The developed model is simulated for a manufacturing workspace scenario considering different task complexities and involving multiple robots to complete shared tasks. The simulation results indicate that for a constant multi-robot performance in operation, the human operator's trust in robots' operation improves whenever the comparative performance of the robots improves with respect to the human operator performance. The impact of robot hypothetical learning capabilities on human trust in the same HRC setting is also analyzed. The results confirm that a hypothetical learning capability allows robots to reduce human workloads, which improves human performance. The simulation result analysis confirms that the human operator's trust in the multi-robot operation increases faster with the improvement of the multi-robot performance when the robots have a hypothetical learning capability. An empirical study was conducted involving a human operator and two collaborator robots with two different performance levels in a software-based HRC setting. The experimental results closely followed the pattern of the developed mathematical models when capturing human trust and performance in terms of human-multi-robot collaboration.
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Why is it important?
Rapid developments in robotic technologies have enabled their close collaboration with human operators and laid the grounds for effective, safe, and reliable applications of trustworthy autonomy in human-robot interactions. Human-robot Collaboration (HRC) could enhance the joint performance of the human operators and the robots to complete the assigned work. A key factor that facilitates the collaboration of human operators and robots is the trust that they develop in each other. From this brief review, we realize that (1) most existing trust models are case-specific and difficult to extend/generalize for different scenarios, (2) many of these models do not directly take human performance into account, and instead use other factors such as human emotion, teammate's capabilities, fault occurrences in the robot, or only the robot performance, (3) most of these existing trust models are suitable for static environments only which are incapable of describing the evolution of the trust, and (4) most of these models do not quantitatively include the effects of complexity of the tasks and the rate of human cognitive utilization on human performance within the Human-robot Collaboration settings. In order to address these challenges, the contributions of this article briefly include the following: • Development of a time-driven human performance model that considers the effects of physical and cognitive constraints and factors such as muscle fatigue and recovery, muscle isometric force, human (cognitive and physical) workload, and workload added due to robots' mistakes, and task complexity. • Development of a multi-robot performance model taking into account the rate of task assignment and completion as well as the mistake probabilities of the individual robots. • Development of a model for human operator's trust in HRC settings with single or multiple robots and over different operation regions (depending upon the comparative performance of the human operator and the robots), namely unpredictable region, predictable region, dependable region, and faithful region. • Developed a software-based HRI experimental setup and validated the developed mathematical model through empirical studies. Further, an analysis of the effect of robots' change of performance (e.g., using a learning mechanism) on human performance and their trust in robots within the proposed multi-robot HRC setting has also been included in this research work.
Perspectives
In this research work, a novel time-driven trust model was developed that considered the collaborator performance within a human-multi-robot collaboration framework. The developed model considered the human model in terms of physical performance and cognitive performance, and the multi-robot performance model to quantify trust in the robots' operation. In this perspective, the human performance model was developed in terms of the physical and cognitive constraints and factors such as muscle fatigue and recovery, muscle isometric force, human (cognitive and physical) workload, cognitive utilization factor, and workload due to the robots’ mistakes, and task complexity. The proposed multi-robot performance model was developed in terms of the rate of task allocation and execution, task assignment, and the mistake probabilities of the individual robots. The trust model was investigated in a manufacturing workspace scenario to show the trust development for four different robot operation regions namely, unpredictable region, predictable region, dependable region, and faithful region. The results that are calculated from the mathematical models showed that human trust in robots' operation improved as the comparative performance of robots exceeded human performance. The proposed model was also evaluated for the impacts of the hypothetical learning capabilities of the robots on human trust in the robots' operation. The results showed that the robots' hypothetical learning capabilities reduced the human operator's workload, and so improved the human operator's performance, which in turn enhanced the trust in the robots' operation. This was more evident for the tasks with higher complexities. In addition, the robots with hypothetical learning capabilities perform better than the ones without hypothetical learning capabilities, leading to less human workload and improved human performance values, resulting in faster development of trust in the multi-robot operation. Furthermore, an empirical study was conducted by developing a software-based HRC setting to validate the results of the proposed mathematical model in terms of capturing the human operators' trust in robot performance. The experimental results validated the model and followed the same patterns as the output of the developed model. Further, the HRC setting in the developed model can capture the collaboration of a human operator with single or multiple robots. A future direction of this research will be extending the developed model to the case with multiple human operators, jointly collaborating with multiple robots, which may require incorporating both human-to-human and human-to-robot collaboration factors into the model. The developed model can be also further expanded to include the impact of other factors that potentially contribute to shaping the human operators' trust in robots such as transparency, predictability, user experience, etc.
Md Khurram Monir Rabby
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This page is a summary of: Performance-Aware Trust Modeling Within a Human-Multi-Robot Collaboration Setting, ACM Transactions on Human-Robot Interaction, April 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3660648.
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