Characterizing the Complexity of Social Robot Navigation Scenarios

Social robot navigation algorithms are often demonstrated in overly simplified scenarios, prohibiting the extraction of practical insights about their relevance to real world domains. Our key insight is that an understanding of the inherent complexity of a social robot navigation scenario could help characterize the limitations of existing navigation algorithms and provide actionable directions for improvement. Through an exploration of recent literature, we identify a series of factors contributing to the complexity of a scenario, disambiguating between contextual and robot-related ones. We then conduct a simulation study investigating how manipulations of contextual factors impact the performance of a variety of navigation algorithms. We find that dense and narrow environments correlate most strongly with performance drops, while the heterogeneity of agent policies and directionality of interactions have a less pronounced effect. This motivates a shift towards developing and testing algorithms under higher-complexity settings.

Integrating Open-World Shared Control in Immersive Avatars

Teleoperated avatar robots allow people to transport their manipulation skills to environments that may be difficult or dangerous to work in. Current systems are able to give operators direct control of many components of the robot to immerse them in the remote environment, but operators still struggle to complete tasks as competently as they could in person. We present a framework for incorporating open-world shared control into avatar robots to combine the benefits of direct and shared control. This framework preserves the fluency of our avatar interface by minimizing obstructions to the operator's view and using the same interface for direct, shared, and fully autonomous control. In a human subjects study (N=19), we find that operators using this framework complete a range of tasks significantly more quickly and reliably than those that do not.