Human-Robot Collaboration (HRC) is increasingly common in physically demanding industrial settings. However, the impact of variable human states, such as physical fatigue, on trust and the quality of this collaboration remains unclear. This is especially critical during HRC that entails co-lifting of industrial materials. This study examines the impact of operator physical fatigue on trust in a robotic partner, as well as associated perceptions of robot effort, coordination, lift quality, and objective lift performance during a co-lifting task that requires complex maneuvering. In a within-subjects experiment, 40 healthy adults (20 females) performed a collaborative lifting task through an asymmetrical complex lift trajectory under two conditions: no-fatigue and fatigue, induced via physical exertion of the dominant arm. We collected subjective ratings of trust, perceived exertion, lift quality, robot effort, and coordination, alongside objective performance metrics based on movement trajectories, such as similarity (using dynamic time warping), acceleration, and jerks. The results demonstrate that physical fatigue, as evidenced by increased perceived exertion ratings, significantly decreased participants' trust in the robot (particularly in the late phase), despite no change in the robot's actual performance. Interestingly, the impacts of fatigue on perceptions of effective coordination were dynamically impacted by the early (higher under fatigue) versus the late (lower under fatigue) phases; participants viewed the robot's effort as less consistent only in the late phase when fatigued. Surprisingly, while fatigue did not affect any of the objective performance metrics based on movement trajectories, participants perceived an improvement in transport quality; however, this was only found in the early phase. These findings indicate that physical fatigue alters not only the physical capacity of the operator but also crucial perceptual shifts of the robot partner and perceived transport quality. This highlights the need for more objective metrics to evaluate HRC that are resilient to operator fatigue. Doing so may enable the development of robust HRC systems that ensure effective and trustworthy collaborations.

Trust in human–robot collaboration (HRC) is influenced by situational factors, such as human states of fatigue and robot performance (e.g., reliability). In industrial settings, operators are often fatigued which increases attentional demands. This sustained mental demand may potentially alter the trusting behaviors of the operator. Whether this happens, and why, are currently unexplored. While most studies have relied on self-reports, recent advancements in neuroergonomics have enabled the assessment of neural signatures of trust. We hypothesize that shedding light on the neural mechanisms during HRC, under varying trust states, may provide additional insights (beyond trust perceptions) into the subconscious cognitive or affective states associated with trust under cognitive fatigue. To test this hypothesis, we conducted an HRC experiment on 16 sex-balanced participants in which trust was manipulated via robot reliability at two sessions (fatigue session and no-fatigue session). We monitored brain activity, heart rate variability (HRV), performances, and subjective experiences to investigate the effect of cognitive fatigue and robot reliability on trust metrics in HRC. A significant main effect of cognitive fatigue and reliability was observed on brain activations, functional and effective connectivity, HRV, subjective response, and performance. While most measures were sensitive to changes in cognitive fatigue and reliability, only neural responses revealed interactions between reliability, cognitive fatigue, and sex. Anterior prefrontal cortex (APFC)–left dorsolateral prefrontal cortex (LDLPFC) connection strength decreased with unreliable robot behavior in non-fatigued states but unexpectedly increased when fatigue was present. This suggests a neural strategy shift involving greater top-down influence from executive to motivational regions to exert more mental effort in challenging HRC conditions. Insights from this investigation can advance objective trust measures under cognitive fatigue states and help design better collaborations.

Trust in human–robot collaboration is an essential consideration that relates to operator performance, utilization, and experience. While trust’s importance is understood, the state-of-the-art methods to study trust in automation, like surveys, drastically limit the types of insights that can be made. Improvements in measuring techniques can provide a granular understanding of influencers like robot reliability and their subsequent impact on human behavior and experience. This investigation quantifies the brain–behavior relationships associated with trust manipulation in shared space human–robot collaboration to advance the scope of metrics to study trust. Thirty-eight participants, balanced by sex, were recruited to perform an assembly task with a collaborative robot under reliable and unreliable robot conditions. Brain imaging, psychological and behavioral eye-tracking, quantitative and qualitative performance, and subjective experiences were monitored. Results from this investigation identify specific information processing and cognitive strategies that result in identified trust-related behaviors that were found to be sex specific. The use of covert measurements of trust can reveal insights that humans cannot consciously report, thus shedding light on processes systematically overlooked by subjective measures. Our findings connect a trust influencer (robot reliability) to upstream cognition and downstream human behavior and are enabled by the utilization of granular metrics.

This paper presents the development and validation of a robot adaptation model to support human operators in Human-Robot Collaborative tasks when they are cognitively fatigued. A human-centered robot adaptation method for providing appropriate assistance to the operator is developed with a dual objective of task performance optimization and aiding human cognitive fatigue recovery. The problem is formulated as a Markov Decision Process (MDP) and solved using Q-learning. The implementation issues resulting from modeling the MDP and performing Q-learning for cognitive fatigue recovery, methods to mitigate those issues, and implications on the resulting optimal policies are discussed. The proposed approach is evaluated through a user study of sixteen participants performing a robotic surface polishing task under cognitive fatigue conditions. The MDP model is validated using subjective metrics (fatigue perception surveys) and objective metrics (Heart-Rate Variability (HRV), accuracy in trajectory tracking, and time efficiency of the task). Fatigue perceptions, accuracy, and time efficiency improved during the user-specific optimal adaptation policies. HRV analysis of time-domain features shows an overall improvement in fatigue conditions during the optimal adaptation policies. The results from this approach indicate that such human-centered robot adaptation can lead to efficient human-robot collaborations with robust interactions between robots and humans.