Situated proactive task generation for embodied agents

Embodied agents operating in everyday environments must go beyond explicit instruction following toward proactive assistance, in which they infer latent human needs from ongoing dialogue, ground these needs in the physical scene, and determine whether and how to act. However, existing vision-language-action systems are primarily designed for explicit task execution and struggle to jointly reason over conversational context and dynamic embodied environments. In this work, we introduce HomeSPT-Bench, a benchmark for situated proactive task generation built on the OmniGibson simulator, in which agents must infer implicit needs from multi-party dialogue, ground them in fully specified 3D scenes, and decide on appropriate actions. Evaluations show that state-of-the-art VLM-based agents exhibit a substantial performance gap in this setting, with frequent failures in spatial grounding, occlusion handling, and intervention decision-making. HomeSPT-Bench provides a testbed for studying situated proactive behavior that integrates dialogue understanding with embodied scene grounding, and highlights the remaining gap between language-based reasoning and reliable embodied proactive assistance.