📑 Clare's work on predictability using trajectory optimization for human-robot teaming accepted VAM-HRI!

In this work, we discuss how trajectory optimization can be used to improve the predictability of robots.

Improving Robot Predictability via Trajectory Optimization Using a Virtual Reality Testbed

Clare Lohrmann, Ethan Berg, Bradley Hayes, Alessandro Roncone


The ability to predict where a robot will be next, or how it will navigate an area is critical to safe and effective human-robot collaboration and interaction. Due to information asymmetry, the path that a robot takes may be optimal, yet unpredictable to an observing human who does not have access to the same information. Unpredictability presents a safety risk to humans, and also makes interacting with robots more cognitively intensive and confusing than need be. In this work, we propose an algorithm that optimizes a robot's trajectory for predictable behavior, resulting in a robot that moves in a way that is more predictable to humans, balanced with what is optimal to the robot. To validate this approach, we propose two human-subjects experiments, one of which is conducted in virtual reality.

The full paper can be accessed here and from our Publications tab.