Generative Embodied Multiple Behavior Control Systems for Human-like Agents

2026-09-18

AI Summary

本文通过提出一种结合目标导向和习惯行为控制机制的框架，解决了人类类代理中习惯行为被忽视的问题，提高了3D环境中人类行为模拟的真实性。

Abstract

An enduring and richly elaborated dichotomy in cognitive neuroscience is that of human behavior control mechanisms, divided into habitual versus goal-directed. While existing human-like agent frameworks primarily focus on modeling goal- directed behavior, habitual behavior has been largely overlooked, though it plays a crucial role in human daily life. In this paper, we address this gap by studying multiple behavior control systems that jointly model goal-directed and habitual behaviors. We propose a human behavior control mechanism-inspired framework which the Habitual Controller retrieves cue-triggered behaviors from personal- ized habit memory, while the Goal-directed Controller employs a context-aware world model to predict action consequences and estimate their values. The Arbiter dynamically balances the influence of both systems according to individual differ- ences and momentary internal states. To reconstruct diverse human-level behavior instructions in 3D environments, we further develop a keyframe-guided 3D mo- tion generation module. Through extensive evaluation methods, human studies, and ablations studies, experimental results demonstrate that human-likeness per- formance is significantly improved by our approach. The efficacy of our approach indicates the benefits of leveraging habitual behavior and multiple behavior con- trol system coordination for believable embodied human-like agents.

Problem

• behavior control mechanisms
• habitual behavior
• goal-directed behavior
• human-like agents

Innovation

• Habitual Controller
• Goal-directed Controller
• Arbiter
• Keyframe-guided 3D Motion Generation