Towards Interaction Regulation from Human Feedback via Free Energy Minimization

2026-09-16

AI Summary

研究通过自由能最小化方法，设计了一种控制理论框架，以在线整合人类偏好到自主代理策略中，解决人机交互调节问题。

Abstract

A central challenge across control and learning is the design of mechanisms regulating the interactions between humans and autonomous agents. Inspired by the free energy principle from computational neuroscience, we introduce a control-theoretical framework to integrate human preferences online into an agent policy. We turn the framework into an open control architecture and validate our approach using a human-in-the-loop experimental testbed involving a rover navigating via onboard sensing. The human, remotely located and equipped with virtual reality headsets, shares the same sensory information as the rover. Human preferences are provided to the rover via gestures which introduce both cooperative and competitive interactions between the agent goal and the preferences. The experiments show that interactions are regulated, validating the proposed approach.

Problem

• interaction regulation
• human feedback
• autonomous agents