DDSeVA: Demand-Driven Semantic Value Navigation with Verified Target Acquisition

Demand-driven navigation extends embodied navigation beyond predefined object search, requiring an agent to satisfy open-ended human needs in unknown environments. Existing methods typically map demands to concrete target objects and employ vision-language reasoning techniques to achieve this. However, correct demand-object grounding does not guarantee reliable task completion. The agent must search effectively before any target is observed and decide whether a detected candidate provides sufficient evidence to terminate, a challenge we term the detection-to-success gap. To bridge this gap, we propose DDSeVA, a closed-loop framework that couples Demand-Driven Semantic Value search with verified target Acquisition. During search, DDSeVA maintains a demand-grounded semantic state and ranks candidate locations according to their expected contribution to task success. During acquisition, each target detection is treated as a hypothesis and verified using temporal consistency, learned reachability estimates, and a prediction of whether Done would satisfy the success criterion. Closed-loop evaluation on ProcTHOR in AI2-THOR demonstrates that DDSeVA consistently outperforms existing methods across all four scene-instruction splits. Compared with the current state of the art, DDSeVA improves average navigation success rate relatively by 10.5%, success weighted by path length by 14.6%, and selection success rate by 30.6%. Code and video demos are available at https://anonymous.4open.science/r/DDSeVA-6675.