Overview

With the increasing deployment of artificial intelligence (AI) technologies, the potential of humans working with AI agents has been growing at a great speed. Human-AI teaming is an important paradigm for studying various aspects when humans and AI agents work together. The unique aspect of Human-AI teaming research is the need to jointly study humans and AI agents, demanding multidisciplinary research efforts from machine learning to human-computer interaction, robotics, cognitive science, neuroscience, psychology, social science, and complex systems. However, existing platforms for Human-AI teaming research are limited, often supporting oversimplified scenarios and a single task, or specifically focusing on either human-teaming research or multi-agent AI algorithms. We introduce CREW, a platform to facilitate Human-AI teaming research to engage collaborations from multiple scientific disciplines, with a strong emphasis on human involvement. It includes pre-built tasks for cognitive studies and Human-AI teaming with expandable potentials from our modular design. Following conventional cognitive neuroscience research, CREW also supports multimodal human physiological signal recording for behavior analysis. Moreover, CREW benchmarks real-time human-guided reinforcement learning agents using state-of-the-art algorithms and well-tuned baselines. With CREW, we were able to conduct 50 human subject studies within a week to verify the effectiveness of our benchmark.

Video (Click to YouTube)

Paper

Check out our paper linked here.

Documentation

Check out our full documentation including tutorials to use CREW and detailed descriptions of the designs and structures of CREW at https://generalroboticslab.github.io/crew-docs/

Codebase

Check out our codebase at https://github.com/generalroboticslab/CREW

Citation

@misc{zhang2024crew,
      title={CREW: Facilitating Human-AI Teaming Research}, 
      author={Lingyu Zhang and Zhengran Ji and Boyuan Chen},
      year={2024},
      eprint={2408.00170},
      archivePrefix={arXiv},
      primaryClass={cs.HC},
      url={https://arxiv.org/abs/2408.00170}, 
}        

Acknowledgment

This work is supported in part by ARL under awards W911NF2320182 and W911NF2220113.

Contact

If you have any questions, please feel free to contact Lingyu Zhang.