UniNavManip: Graph-Constrained Belief- Space Control for Unified Mobile Navigation and Manipulation

Long-horizon mobile manipulation must coordinate task progress, visual memory, and heterogeneous base–arm control under partial observability. Existing visuomotor policies often model motion, task structure, or visual history separately, leaving mode transitions and verified progress weakly coupled.We present UNINAVMANIP, a vision-language-action controller that combinesa language grounded TaskGraph,typed head–wrist memory, mode-routed hybrid actions,and receding horizon candidate selection.The graph restricts proposals using verified predecessors and confirmed preconditions; the controller executes short prefixes and updates the graph only after fresh-observation verification. In simulation-only physics-based IsaacSim evaluations on 240 held-out episodes from three scenes, UNINAVMANIP reaches 61.7% complete-task success,compared with 57.1% for the strongest adapted public baseline and 49.6% for a matched internal hierarchical baseline. It also records the lowest collision rate at 0.15 per episode.