Human-Inspired Techniques for Human-Machine Team Planning

Robots are increasingly introduced to work in concert with people in high-intensity domains, such as manufacturing, space exploration and hazardous environments. Although there are numerous studies on human teamwork and coordination in these settings, very little prior work exists on applying these models to human-robot interaction. This paper presents results from ongoing work aimed at translating qualitative methods from human factors engineering into computational models that can be applied to human-robot teaming. We describe a statistical approach to learning patterns of strong and weak agreements in human planning meetings that achieves up to 94% prediction accuracy. We also formulate a human-robot interactive planning method that emulates cross-training, a training strategy widely used in human teams. Results from human subject experiments show statistically significant improvements on team fluency metrics, compared to standard reinforcement learning techniques. Results from these two studies support the approach of modeling and applying common practices in human teaming to achieve more effective and fluent human-robot teaming.