Active Perception Framework for Embodied Target Disambiguation

A novel framework aimed at enhancing active perception in robotics has been introduced to tackle target ambiguity in embodied settings. This framework, outlined in a paper on arXiv (2608.13605), prioritizes active observation for gathering information, while incorporating a vision-language model that determines whether to persist in observation, seek clarification, or finalize target selection based on gathered visual and interaction data. The need for this approach stems from the understanding that ambiguity can result from both user intent and the absence of pertinent physical evidence during observations, such as occlusions, limited viewpoints, unreadable text, or unobserved targets. Unlike existing methods that often depend on user clarification, this new framework empowers robots to adapt their observations to retrieve missing discriminative evidence, enhancing the use of natural language as a task interface in intricate environments.