OmniBrain-0: A Unified Agentic Model for Embodied Intelligence

General-purpose embodied intelligence requires not only individual skills but also long-horizon planning, skill coordination, and recovery toward task goals. Specialist robot policies alone do not ensure task-level reliability, while powerful multimodal agents depend on costly long-context reasoning and external experience that compact models cannot readily reproduce. We introduce **OmniBrain-0**, a lightweight unified embodied agent that brings manipulation and navigation into a shared decision framework. Rather than replacing specialist controllers, OmniBrain-0 orchestrates heterogeneous skills through a single high-level model, integrating task understanding, planning, progress tracking, and feedback-driven recovery. To transfer and integrate these capabilities, we develop a progressive training framework combining embodied pretraining, domain-specialized agent distillation, and multi-expert consolidation with MOPD. This framework first converts powerful teachers' interaction experience into specialized decision capabilities, then consolidates manipulation and navigation expertise into a compact unified agent. Crucially, unification occurs within the learned decision model rather than only at the tool interface. We evaluate OmniBrain-0 on LIBERO-Pro, RoboCasa365, RoboTwin 2.0, and R2R-CE Val-Unseen, covering manipulation and navigation. We further evaluate OmniBrain-0 on three real-world manipulation tasks, demonstrating its applicability to physical robot execution. Results show strong performance across both domains and substantial gains over the compact zero-shot backbone, supporting effective transfer and consolidation of agentic planning and recovery capabilities across task domains.