HiBerNAC: Hierarchical brain-inspired robotic neural agent collective for disentangling complex manipulation

Recent advances in multimodal vision language action (VLA) models have revolutionized traditional robot learning, enabling systems to interpret vision, language, and action in unified frameworks for complex task planning. However, mastering complex manipulation tasks remains an open challenge, constrained by limitations in persistent contextual memory, multi-agent coordination under uncertainty, and dynamic long-horizon planning across variable sequences. To address this challenge, we draw conceptual inspiration from principles of neural organization and hierarchical decision-making, using them as architectural design metaphors rather than as claims of biologically faithful neural emulation. We present HiBerNAC, a hierarchical brain-inspired robotic neural agent collective that combines: (1) multimodal VLA planning and reasoning with (2) brain-inspired reflection and multi-agent mechanisms, organized as a modular multi-agent architecture conceptually motivated by principles of neural organization, specifically designed for complex robotic manipulation tasks. By leveraging brain-inspired functional modules with decentralized multi-agent collaboration, our approach enables robust and enhanced real-time execution of complex manipulation tasks. In addition, the agentic system has scalable collective intelligence via dynamic agent specialization, adapting its coordination strategy to variable task horizons and complexity. Through extensive experiments (conducted in a MuJoCo-based simulation benchmark) on complex manipulation tasks compared with the state-of-the-art VLA models, we demonstrate that HiBerNAC reduces average long-horizon task completion time by 23%, and achieves non-zero success rates (8%–48%) on multi-path tasks where prior state-of-the-art VLA models consistently failed (differences validated by two-sided Welch’s t -tests, p < 0.05 for correction, OOD, multimodal, and long-horizon categories). These results suggest that principles of neural organization can serve as useful inductive biases for designing robotic manipulation systems. More details are available at our project website .