Problem
Research questions and friction points this paper is trying to address.
affective motion
humanoid robots
valence-arousal
real-time generation
parameterization
PAMoR: Parameterized Affective Motion Generation in Real Time for Humanoid Robots
🤖 AI Summary
本文提出PAMoR方法，通过将情感转化为可量化的控制参数来实现实时生成具有情感的人形机器人动作。
📝 Abstract
People read a humanoid robot's motion in social settings not only for the action performed but for the affect conveyed. Motion carrying that affect has so far been generated for human avatars, where style is taken from a reference clip or an emotion word, neither of which can be quantitatively parameterized. We present PAMoR, which turns affect into a measured control parameter: a valence-arousal (V-A) coordinate computed natively on robot kinematics. It is obtained in closed form from postural expansion and movement energy, and these measurements serve directly as generation conditions, with no human annotation. An action prior and two affect priors, trained in a shared latent space, are composed at each denoising step: the action prior fixes what is performed, the affect priors modulate how. Whole-body motion rolls out autoregressively on a 29-DoF Unitree G1 in real time, with action and affect both editable. Generated motion tracks the commanded V-A over its full range while text-to-motion fidelity still matches text-only baselines. In a perceptual study, raters identify the commanded emotion on 0.38 of trials, above both baselines and approaching the 0.44 reported for acted human bodies.
Innovation
Methods, ideas, or system contributions that make the work stand out.
valence-arousal coordinate
real-time motion generation
affective motion
autoregressive rollout
humanoid robots
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