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Robots are an increasingly common presence in human environments, working alongside people in factories, hospitals, and military field operations. However, today people must change how they work to accommodate robots in their workspace. This poses a significant barrier to adoption of robot technology by creating inefficiencies. This project provides an integrated research and educational approach to develop intelligent robotic technologies that more seamlessly integrate with human work environments.
The technical approach translates qualitative and quantitative insights from human studies into explicit computational models, and exploits these models to redesign robot algorithms for learning, decision-making, and control. The research effort specifically investigates three types modifications to robot behavior: (1) modifying robot motion planning using anticipatory signals of human motion, (2) customizing robot task plans using statistical models of human task execution, and (3) inferring and applying human domain expertise to expedite automated planning for mixed human-robot teams. Human subject experimentation is planned to assess ease-of-interaction, worker trust, and task performance, and the approach is validated using metrics to quantitatively assess the degree to which a robot's behavior preserves natural human workflow. By designing robot autonomy that minimizes disruption to human workflow, the approach supports graceful transitions from robotic work back to human work and vice versa.