Social Momentum

Intent-expressive robot motion has been shown to result in increased efficiency and reduced planning efforts for copresent humans. Existing frameworks for generating intent-expressive robot behaviors have typically focused on applications in static or structured environments. Under such settings, emphasis is placed towards communicating the robot»s intended final configuration to other agents. However, in dynamic, unstructured and multi-agent domains, such as pedestrian environments, knowledge of the robot»s final configuration is not sufficiently informative as it completely ignores the complex dynamics of interaction among agents. To address this problem, we design a planning framework that aims at generating motion that clearly communicates an agent»s intended collision avoidance strategy rather than its destination. Our framework estimates the most likely intended avoidance protocols of others based on their past behaviors, superimposes them, and generates an expressive and socially compliant robot action that reinforces the expectations of others regarding these avoidance protocols. This action facilitates inference and decision making for everyone, as illustrated in the simplified topological pattern of agents» trajectories. Extensive simulations demonstrate that our framework consistently achieves significantly lower topological complexity, compared against common benchmark approaches in multi-agent collision avoidance. The significance of this result for real world applications is demonstrated by a user study that reveals statistical evidence suggesting that multi-agent trajectories of lower topological complexity tend to facilitate inference for observers.