GestAdapt：面向人形机器人的工作空间条件化共语手势生成
GestAdapt: Workspace-Conditioned Co-Speech Gesture Generation for Humanoid Robots
- Tilburg University（蒂尔堡大学）
- TU-Dresden（德累斯顿工业大学）
- China University of Mining and Technology-Beijing (CUMTB)（中国矿业大学（北京））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
GestAdapt提出工作空间条件化框架，通过共享运动表示从多语料库学习，生成适应手腕工作空间的共语手势，并在用户研究和真实机器人评估中优于无约束基线。
AI中文摘要：
机器人的共语手势不仅需要适应语音和具身形态，还必须适应执行动作时可用的工作空间。由于相同的语音可以伴随不同的手势，机器人可以响应工作空间约束，例如，在靠近墙壁时，为语音生成手势。在这些场景中，机器人应以合适的动作进行手势表达，而不是简单地修正无约束的手势。为实现这一目标，我们提出了GestAdapt，一个以工作空间为条件的框架，该框架将共语手势生成条件化于预设的手腕工作空间。GestAdapt框架通过共享的运动表示从六个互补的共语语料库中学习，并支持重定向到不同的机器人具身形态。定量评估表明，生成的运动在尊重工作空间的同时，仍接近真实运动分布。在用户研究中，在修改后的工作空间约束下生成的手势获得了3.24/5的平均质量评分，高于我们无工作空间变体的评分（2.43/5），但低于参考动作的评分（3.68/5）。在真实机器人评估中，所有比较的动作均在相同的工作空间约束下重定向到Reachy2人形机器人。使用我们框架生成的动作在69.7%的比较中排名第一，高于我们无工作空间变体的基线和事后约束的重定向真实动作。总体而言，结果支持在生成过程中适应可用工作空间来调整手势，而不是事后修改无约束轨迹以满足工作空间约束，从而可能损害手势的自然性。
英文摘要：
Co-speech gestures for robots must adapt not only to speech and embodiment, but also to the workspace available for performing the motion. Since the same speech can be accompanied by different gestures, a robot can respond to workspace constraints, e.g., gestures for speech next to a wall. In these scenarios, the robot should gesture in a suitable motion rather than simply correcting an unconstrained one. To achieve this goal, we present GestAdapt, a workspace-conditioned framework that conditions co-speech gesture generation on a prescribed wrist workspace. The GestAdapt framework learns from six complementary co-speech corpora through a shared motion representation and supports retargeting to different robot embodiments. Quantitative evaluation shows that generated motions remain close to the real-motion distribution while respecting the workspace. In a user study, gestures generated under modified workspace constraints receive a mean quality score of 3.24/5, above our no-workspace variant (2.43/5) and below the reference motions (3.68/5). In a real robot evaluation, all compared motions are retargeted to the Reachy2 humanoid robot under identical workspace constraints. Motions generated with our framework rank first in 69.7\% of comparisons, higher than our no-workspace variant baseline and retargeted ground-truth motions constrained afterward. Overall, the results support adapting gestures to the available workspace during generation, rather than modifying unconstrained trajectories afterward to satisfy workspace constraints, potentially compromising gesture naturalness.
