用运动表达机器人感知不确定性，让协作更自然。
Anatomy of Uncertainty: Expressive Descriptors of Robotic Manipulator Motion for Non-verbal Communication in Human-Robot Collaboration
- 基于拉班动作分析构建信心-警觉状态空间，映射五类不确定性行为
- 设计五种运动基元，通过11个参数描述动作特征，如加速度、凝视角等
- 实验验证人类可准确识别意图，关键参数显著影响表达强度
在人机协作中，机器人不仅需传达行动意图，还需表达因感知不全或模糊带来的不确定性。本文提出一种数学框架，通过机械臂运动表征感知不确定性。借鉴拉班动作分析，将机器人行为组织于‘承诺-警觉’状态空间中，对应信心、好奇、犹豫、恐惧和静止等不确定性状态，并映射为特定的拉班努力符号。设计了五种运动基元——接近、暂停、后退、探索与振颤，采用11个运动学与几何描述符进行参数化，包括加速度、停顿与后退特征、视线角度、倾斜度及震颤幅度。通过视频人体实验评估四种表达轨迹的识别效果及各描述符对感知强度的影响。参与者能可靠识别出预期行为状态，多个描述符显著调节表达效果。结果为在运动中编码机器人不确定性提供了感知基础，支持未来在共享环境中使用参数化运动表示实现自主轨迹生成。代码、视频、问卷与附录见：https://bit.ly/github-aou。
原文摘要 · Abstract (English)
Robots operating in human-robot collaboration must communicate not only their intended actions but also uncertainty arising from incomplete or ambiguous perception. This work introduces a mathematical framework for expressing perceptual uncertainty through robotic manipulator motion. Drawing on Laban Movement Analysis, robot behavior is organized in a Commitment-Vigilance state space that maps uncertainty-related states - confidence, curiosity, hesitance, fear, and inactivity - to distinct Laban Effort signatures. Five motion primitives - approach, pause, retreat, exploration, and oscillation - are then parameterized using eleven kinematic and geometric descriptors, including acceleration, pause and retreat characteristics, gaze angles, tilt, and shiver amplitude. A video-based human-subject study evaluated recognition of four expressive trajectories and the influence of individual descriptors on perceived intensity. Participants reliably identified the intended behavioral states, while several descriptors significantly modulated expressiveness. The results establish a perceptually grounded basis for encoding robot uncertainty in motion and support future autonomous trajectory generation using parametric movement representations for collaborative tasks in shared environments. Code, videos, questionnaire and appendices are available at "https://bit.ly/github-aou".
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