Uncertainty-Resilient Active Intention Recognition for Robotic Assistants

-- Purposeful behavior in robotic assistants requires the integration of multiple components and technological advances. Often, the problem is reduced to recognizing explicit prompts, which limits autonomy, or is oversimplified through assumptions such as near-perfect information. We argue that a critical gap remains unaddressed - specifically, the challenge of reasoning about the uncertain outcomes and perception errors inherent to human intention recognition. In response, we present a framework designed to be resilient to uncertainty and sensor noise, integrating real-time sensor data with a combination of planners. Our integrated framework has been successfully tested on a physical robot with promising results. Robotic assistants may be integrated into modern industrial environments, e.g., delivering tools, parts or modules interleaved with tidying the workspace. Such tasks, however, require a combination of robust planning, navigation, grasping, and perception-particularly when explicit commands are not available and the robot must identify and pursue goals, in collaborative spaces shared with people.