Uncertainty-Resilient Active Intention Recognition for Robotic Assistants
This addresses the challenge of uncertainty in human intention recognition for robotic assistants, though it appears incremental as it builds on existing POMDP methods.
The paper tackles the problem of robotic assistants recognizing human intentions under uncertainty and sensor noise by presenting a framework that integrates real-time sensor data with planners centered around an intention-recognition POMDP, achieving promising results in physical robot tests.
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. Centered around an intention-recognition POMDP, our approach addresses cooperative planning and acting under uncertainty. Our integrated framework has been successfully tested on a physical robot with promising results.