CALM：机器人接近过程中配置感知的人类干预边界
CALM: Configuration-Aware Human Intervention Boundaries During Robot Approach
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中文总结 AI 辅助
本研究通过41人实验发现机器人手臂配置影响停止距离，提出配置感知极限模型（CALM）生成干预边界，支持将身体配置作为规划变量。
中文摘要 AI 辅助
机器人身体配置如何影响接近过程中的人类干预仍未被充分探索。我们开展了一项受试者内研究，共有41名参与者，测量了四种人形机器人手臂配置和两种空间尺度下的最终停止距离、主观舒适度以及探索性眼动追踪响应。手臂完全向前伸展相比手臂下垂，使停止距离增加了约31-36厘米。空间尺度主要影响舒适度和瞳孔响应，而未检测到停止距离的显著变化。我们提出了配置感知极限模型（CALM），该模型将停止距离分布转化为依赖于配置的人群覆盖边界。在80%覆盖率下估计的边界范围为0.88至1.47米。在一项说明性的一维规划分析中，重新配置使得一个1.10米的接近目标得以实现，而在相同的标称点态20%干预概率约束下，手臂保持完全伸展时该目标无法达到。这些发现支持将身体配置视为规划变量，同时区分物理安全、行为干预和主观成本。
英文摘要
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.
发表机构
- School of Architecture, Tsinghua University（清华大学建筑学院）
- Architectural Design & Research Institute of Tsinghua University（清华大学建筑设计研究院）
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