Generative Embodied Multiple Behavior Control Systems for Human-like Agents

Abstract

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}.