ParaEQsA: Parallel and Asynchronous Embodied Questions Scheduling and Answering
This work addresses the need for responsive and efficient embodied agents in real-world deployments with multi-question workloads, representing an incremental advancement over classical single-question EQA.
The paper tackles the problem of handling multiple asynchronous and urgency-varying questions in embodied question answering, introducing the ParaEQsA framework that outperforms sequential baselines by reducing exploration and delay with metrics like Direct Answer Rate and Normalized Urgency-Weighted Latency.
This paper formulates the Embodied Questions Answering (EQsA) problem, introduces a corresponding benchmark, and proposes a system to tackle the problem. Classical Embodied Question Answering (EQA) is typically formulated as answering one single question by actively exploring a 3D environment. Real deployments, however, often demand handling multiple questions that may arrive asynchronously and carry different urgencies. We formalize this setting as Embodied Questions Answering (EQsA) and present ParaEQsA, a framework for parallel, urgency-aware scheduling and answering. ParaEQsA leverages a group memory module shared among questions to reduce redundant exploration, and a priority-planning module to dynamically schedule questions. To evaluate this setting, we contribute the Parallel Asynchronous Embodied Questions (PAEQs) benchmark containing 40 indoor scenes and five questions per scene (200 in total), featuring asynchronous follow-up questions and urgency labels. We further propose metrics for EQsA performance: Direct Answer Rate (DAR), and Normalized Urgency-Weighted Latency (NUWL), which jointly measure efficiency and responsiveness of this system. ParaEQsA consistently outperforms strong sequential baselines adapted from recent EQA systems, while reducing exploration and delay. Empirical evaluations investigate the relative contributions of priority, urgency modeling, spatial scope, reward estimation, and dependency reasoning within our framework. Together, these results demonstrate that urgency-aware, parallel scheduling is key to making embodied agents responsive and efficient under realistic, multi-question workloads.