EMoG: Emotion-Modulated Gait Generation for Expressive Humanoid Locomotion

2026-09-13

AI Summary

本文提出EMoG框架，通过情绪调节步态生成解决人形机器人行走表达性问题，利用MLP和强化学习实现实时情感风格步态轨迹。

Abstract

Existing humanoid locomotion systems primarily focus on stability and task execution, while integrating expressiveness with explicit locomotion control remains challenging. We propose EMoG, an emotion-modulated gait generation framework for expressive humanoid locomotion. EMoG introduces an emotional-style code with continuously adjustable intensity. Conditioned on this code and physical commands, a lightweight MLP generates expressive, command-consistent periodic gait trajectories in real time, which are tracked by a unified reinforcement learning policy for physical execution. To support training, we collect a large-scale emotion-annotated gait dataset from professional performers and develop an automated pipeline to extract physically consistent periodic gait cycles. EMoG also integrates an LLM-based parser that converts free-form language into emotional style and motion parameters for interactive control. Experiments demonstrate continuous gait-style modulation with perceptible expressive cues while maintaining command tracking. EMoG provides a practical approach to parameterized emotional-style walking for human-robot interaction.

Problem

• humanoid locomotion
• expressiveness
• emotion-modulated
• gait generation

Innovation

• emotion-modulated gait
• expressive humanoid locomotion
• emotional-style code
• MLP-generated gait trajectories
• LLM-based parser