aiqbench analysis
This research presents EMoG, a framework that uses machine learning and reinforcement learning to generate expressive gaits for humanoid robots based on emotional inputs. By integrating an LLM-based parser and emotion-annotated datasets, the system enables real-time modulation of locomotion styles while maintaining physical stability and command tracking. This work advances human-robot interaction by moving beyond purely functional movement toward emotionally resonant robotic behavior.
EMoG: Emotion-Modulated Gait Generation for Expressive Humanoid Locomotion
arXiv:2609.14432v1 Announce Type: new 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.
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.
Robotics (cs.RO)
arXiv:2609.14432 [cs.RO]
(or arXiv:2609.14432v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.14432 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 234 words
Related coverage
Handroid: Bridging Dexterous Hand and Humanoid
arXiv · Jul 20, 2026
Unleashing Infinite Motion: Scaling Expressive Quadrupedal Motion via Generative Video Priors
arXiv · Sep 10, 2026
Walk Like This: Stylized Locomotion for Physics-Based Characters through Example-Driven Diffusion
arXiv · Oct 14, 2026
EmoPose: Vision-Language Model Guided Emotion-Aware Gesture Generation for Humanoid Robots
arXiv · Sep 22, 2026