ActiveVOI: Value of Information Guided Active Knowledge Acquisition for Open-World Embodied Lifted Regression Planning
Xiatoian Liu, Ali Pesaranghader, Jaehong Kim, Tanmana Sadhu, Hyejeong Jeon, Scott Sanner
The ability to actively acquire information is essential for open-world planning under partial observability and incomplete knowledge. Existing embodied AI systems typically rely on passive strategies that exhaustively collect object and relational information. However, such passive knowledge acquisition becomes impractical in visually complex domains. For instance, a typical household may contain hundreds of uniquely configured objects with unique configurations. Therefore, open-world agents must be able to actively identify which objects are relevant to the task at hand. In this work, we introduce ActiveVOI, a novel zero-shot framework for open-world embodied planning that emphasizes object-centric active knowledge acquisition. ActiveVOI leverages Lifted Regression to generate compact subgoal descriptions that identify task-relevant objects. It also provides a principled approach to quantify the utility of sensing objects using the theory of Value of Information (VOI), guided by commonsense knowledge from large language and vision-language models (LLMs/VLMs). ActiveVOI is evaluated on the visual ALFWorld benchmark, showing substantial improvements over existing LLM- and VLM-based planning methods, and notably even outperforming VLMs that are fine-tuned on ALFWorld data. This work establishes a principled foundation for building embodied agents that actively and efficiently acquire knowledge to plan in open-world environments.