机器人手臂姿态影响人类干预，可据此优化安全距离设计
CALM: Configuration-Aware Human Intervention Boundaries During Robot Approach
- 基于41人实验，建模不同手臂姿态下的干预边界
- 手臂前伸使停止距离增加31-36厘米，空间尺度影响舒适度
- 适用于人机协作中需考虑身体姿态的路径规划场景
机器人本体姿态如何影响人类干预行为仍缺乏研究。我们对41名参与者进行了组内实验，测量了四种类人臂姿态与两种空间尺度下最终停止距离、主观舒适度及探索性眼动响应。完全前伸手臂相比下垂姿态使停止距离增加约31-36厘米；空间尺度主要影响舒适度和瞳孔反应，但未显著改变停止距离。我们提出配置感知极限模型（CALM），将停止距离分布转化为依赖姿态的人群覆盖边界。80%覆盖率下的估计边界范围为0.88至1.47米。在一维规划示例中，通过重新配置可实现1.10米的接近目标，而保持手臂完全前伸时在相同20%干预概率约束下无法达成。结果表明应将身体姿态作为规划变量，并区分物理安全、行为干预与主观成本。
原文摘要 · Abstract (English)
How robot body configuration shapes human intervention during approach remains underexplored. We conducted a within-participants study with 41 participants, measuring final stopping distance, subjective comfort, and exploratory eye-tracking responses across four humanoid arm configurations and two spatial scales. Full forward arm extension increased stopping distance by approximately 31-36 cm relative to arms-down. Spatial scale primarily affected comfort and pupil responses without a detectable stopping-distance shift. We introduce the Configuration-Aware Limit Model (CALM), which translates stopping-distance distributions into configuration-dependent population-coverage boundaries. Estimated boundaries at 80% coverage ranged from 0.88 to 1.47 m. In an illustrative one-dimensional planning analysis, reconfiguration enabled a 1.10 m approach goal that was unreachable with arms remaining fully extended under the same nominal pointwise 20% intervention-probability constraint. These findings support treating body configuration as a planning variable while distinguishing physical safety, behavioral intervention, and subjective cost.
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