发表机构
University of Bristol; University of Copenhagen; Clemson University（布里斯托大学; 哥本哈根大学; 克莱姆森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对类人智能体忽视习惯性行为的问题，提出融合习惯性与目标导向控制的多行为系统框架，并引入关键帧引导的3D动作生成，显著提升类人表现。
AI 中文摘要
认知神经科学中一个持久且被详尽阐述的二分法是人类行为控制机制，分为习惯性行为与目标导向行为。虽然现有的类人智能体框架主要侧重于对目标导向行为进行建模，但习惯性行为在很大程度上被忽视了，尽管它在人类日常生活中扮演着至关重要的角色。在本文中，我们通过研究联合建模目标导向行为和习惯性行为的多行为控制系统来解决这一空白。我们提出了一个受人类行为控制机制启发的框架，其中习惯性控制器从个性化习惯记忆中检索由线索触发的行为，而目标导向控制器采用上下文感知的世界模型来预测行动后果并评估其价值。仲裁器根据个体差异和瞬时内部状态动态平衡两个系统的影响。为了在3D环境中重建多样化的人类级行为指令，我们进一步开发了一个关键帧引导的3D动作生成模块。通过广泛的评估方法、人类研究和消融研究，实验结果表明我们的方法显著提升了类人性能。我们方法的有效性表明，利用习惯性行为和多行为控制系统协调对于可信的具身类人智能体具有益处。
英文摘要
Building human-like agents that reproduce human behavior in realistic 3D environments has been a longstanding objective in AI. Existing human-like agent frameworks primarily focus on modeling goal-directed behavior. However, cognitive neuroscience commonly believes that human behaviors are more likely controlled by multiple control systems, including goal-directed and habitual behavior control systems. Habitual behavior has been largely overlooked though it plays a crucial role in human daily life. In this paper, we address this gap by proposing a multiple control systems setup that jointly models goal-directed and habitual behaviors. Building on this setup, we propose GEMS, in which the Habitual Controller retrieves habitual actions from habit memory in response to relevant environmental stimuli, while the Goal-directed Controller proposes goal-directed actions and estimates their values. The Arbiter dynamically governs the relative influence of each controller and selects the final action. To construct diverse human-level behavior instructions in 3D environments, we further develop a keyframe-guided motion generation module. Extensive quantitative evaluations, human and ablation studies demonstrate that human-likeness performance is substantially improved by GEMS. The efficacy of GEMS indicates the benefits of leveraging habitual behavior and multiple behavior control system coordination for believable embodied human-like agents. The code is available at \href{https://anonymous.4open.science/r/review-video-82f4/demo.mp4}{this link}.
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