RoboMMT: Learning Stable Tracking of Modulated Expressive and Interactive Robot Motion
In this paper, we present RoboMMT (Robot Modulated Motion Tracking), a modular control architecture that couples parameterized expressive motion synthesis, physics-based character control, and reactive adaptation to spatial interaction goals.
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Key points
- Expressive robot motion can improve naturalness, effectiveness, and perceived likability in human-robot interaction.
- When deployed on dynamically unstable platforms, however, reactive execution of expressive body motion may bring the robot to its stability limits, especially when expressivity is decoupled from physical control.
- Evaluations on a quadruped and two humanoid robots show that the learned tracker remains stable and accurate across the entire modulation domain, whereas trackers trained without this coverage lose balance, tracking accuracy, or both.
- We show that layering closed-loop goal adaptation over this foundation enables interactive facing and pointing behaviors, while maintaining stable execution and comparable reference-tracking fidelity.
Sources (1)
- [1]RoboMMT: Learning Stable Tracking of Modulated Expressive and Interactive Robot MotionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 01:47 PM
In this paper, we present RoboMMT (Robot Modulated Motion Tracking), a modular control architecture that couples parameterized expressive motion synthesis, physics-based character control, and reactive adaptation to spatial interaction goals.
Expressive robot motion can improve naturalness, effectiveness, and perceived likability in human-robot interaction.
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