From the paper:
Intent-expressive robot motion has been shown to result in increased efficiency and reduced planning efforts for human or robot partners in implicitly or explicitly collaborative scenarios. 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 focus on the generation of motion that communicates an agent’s intention toward a socially compliant collision avoidance strategy rather than its destination. We contribute a planning framework that estimates the intended avoidance strategies of others, superimposes them, and generates an expressive, socially compliant robot action that reinforces the expectations of others regarding the scene evolution. This action facilitates inference and decision making for everyone, as illustrated in the simplified topological pattern of agents’ trajectories at the end of the execution. Extensive simulations demonstrate that our framework consistently achieves significantly lower topological complexity, compared against common benchmark approaches in the area of multi-agent collision avoidance. The significance of this result for real world applications is demonstrated by a user study that revealed statistical evidence suggesting that multi-agent trajectories of lower topological complexity tend to enable observers to infer the intentions of actors more quickly and more accurately.