HiBerNAC：用于解耦复杂操作的分层脑启发式机器人神经智能体集体
HiBerNAC: Hierarchical Brain-inspired Robotic Neural Agent Collective for Disentangling Complex Manipulation
AI总结：
针对复杂操作中记忆、协调和长时程规划的挑战，提出分层脑启发式神经智能体集体HiBerNAC，结合VLA与神经启发机制，实现时间减少23%并在多路径任务上取得非零成功率。
摘要：
多模态视觉-语言-动作（VLA）模型的最新进展彻底改变了传统机器人学习，使系统能够在统一框架中解释视觉、语言和动作，以完成复杂任务规划。然而，掌握复杂操作任务仍然是一个开放的挑战，受到持续情境记忆的限制、不确定性下的多智能体协调以及跨可变序列的动态长时程规划等约束。为应对这一挑战，我们提出了HiBerNAC，一种受神经科学突破（尤其是神经回路机制和分层决策）启发的分层脑仿真机器人神经智能体集体。我们的框架结合了：(1) 多模态VLA规划与推理，以及(2) 神经启发式反思与多智能体机制，专门针对复杂机器人操作任务设计。通过利用神经启发的功能模块与去中心化的多智能体协作，我们的方法能够实现复杂操作任务的鲁棒且增强的实时执行。此外，该智能体系统通过动态智能体特化展现出可扩展的集体智能，使其协调策略适应可变的任务时域和复杂度。通过与最先进的VLA模型在复杂操作任务上的广泛实验对比，我们证明HiBerNAC将平均长时程任务完成时间减少了23%，并在先前最先进VLA模型持续失败的多路径任务上实现了非零成功率（12%–31%）。这些结果为桥接生物认知与机器人学习机制提供了指示性证据。
英文摘要：
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 propose \textbf{HiBerNAC}, a \textbf{Hi}erarchical \textbf{B}rain-\textbf{e}mulated \textbf{r}obotic \textbf{N}eural \textbf{A}gent \textbf{C}ollective, inspired by breakthroughs in neuroscience, particularly in neural circuit mechanisms and hierarchical decision-making. Our framework combines: (1) multimodal VLA planning and reasoning with (2) neuro-inspired reflection and multi-agent mechanisms, specifically designed for complex robotic manipulation tasks. By leveraging neuro-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 exhibits scalable collective intelligence via dynamic agent specialization, adapting its coordination strategy to variable task horizons and complexity. Through extensive experiments on complex manipulation tasks compared with state-of-the-art VLA models, we demonstrate that \textbf{HiBerNAC} reduces average long-horizon task completion time by 23\%, and achieves non-zero success rates (12\textendash 31\%) on multi-path tasks where prior state-of-the-art VLA models consistently fail. These results provide indicative evidence for bridging biological cognition and robotic learning mechanisms.
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