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Effective human-AI collaboration requires agents to adopt their roles and levels of support based on human needs, task requirements, and complexity. Traditional human-AI teaming often relies on a pre-determined robot communication scheme, restricting teamwork adaptability in complex tasks. Leveraging the strong communication capabilities of Large Language Models (LLMs), we propose a Human-Robot Teaming Framework with Multi-Modal Language feedback (HRT-ML), a framework designed to enhance human-robot interaction by adjusting the frequency and content of language-based feedback. The HRT-ML framework includes two core modules: a Coordinator for high-level, low-frequency strategic guidance and a Manager for task-specific, high-frequency instructions, enabling passive and active interactions with human teammates. To assess the impact of language feedback in collaborative scenarios, we conducted experiments in an enhanced Overcooked-AI game environment with varying levels of task complexity (easy, medium, hard) and feedback frequency (inactive, passive, active, superactive). Our results show that as task complexity increases relative to human capabilities, human teammates exhibited stronger preferences toward robotic agents that can offer frequent, proactive support. However, when task complexities exceed the LLM's capacity, noisy and inaccurate feedback from superactive agents can instead hinder team performance, as it requires human teammates to increase their effort to interpret and respond to the large amount of communications, with limited performance return. Our results offer a general principle for robotic agents to dynamically adjust their levels and frequencies of communication to work seamlessly with humans and achieve improved teaming performance.
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Task Interdependence in Human-Robot Teaming
Human-robot teaming is becoming increasingly common within manufacturing processes. A key aspect practitioners need to decide on when developing effective processes is the level of task interdependence between human and robot team members. Task interdependence refers to the extent to which one’s behavior affects the performance of others in a team. In this work, we examine the effects of three levels of task interdependence—pooled, sequential, reciprocalin human-robot teaming on human worker’s mental states, task performance, and perceptions of the robot. Participants worked with the robot in an assembly task while their heart rate variability was being recorded. Results suggested human workers in the reciprocal interdependence level experienced less stress and perceived the robot more as a collaborator than other two levels. Task interdependence did not affect perceived safety. Our findings highlight the importance of considering task structure in human-robot teaming and inform future research on and industry practices for human-robot task allocation.
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- Award ID(s):
- 1822872
- PAR ID:
- 10176315
- Date Published:
- Journal Name:
- IEEE International Conference on Robot and Human Interactive Communication
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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