Physical human-robot interaction (pHRI) in real-world settings exposes operators to fluctuating environmental conditions during contact-rich tasks. Traditional task-centric evaluations overlook the physiological burdens imposed by these stressors. Therefore, we conducted a multimodal empirical study involving contact-rich tracing tasks under 18 distinct combinations of temperature, acoustic noise, and illuminance. Synchronously, we recorded electrodermal activity (EDA), surface electromyography (sEMG), eye-tracking data, and subjective environmental comfort ratings. Evaluating these physiological signals alongside execution data revealed hidden physiological costs not captured by objective performance. The results revealed that task performance remained stable across all environmental conditions. Autonomic workload, indexed by tonic skin conductance level (SCL), increased with temperature, while physical and cognitive workload were unaffected. Perceived environmental comfort showed no significant association with tracing error or completion time. These findings reveal a compensatory mechanism where operators maintain consistent performance by increasing their physiological effort to suppress thermal discomfort. Such insight motivates the development of physiology-aware control architectures that leverage real-time physiological metrics to reduce operator workload in unstructured environments.

翻译：现实环境中的物理人机交互（pHRI）在高接触型任务中使操作人员暴露于波动的环境条件中。传统的以任务为中心的评估忽视了这些压力源带来的生理负担。为此，我们开展了一项多模态实证研究，在18种不同的温度、噪声和照度组合条件下进行高接触型追踪任务。同步记录了皮肤电活动（EDA）、表面肌电信号（sEMG）、眼动追踪数据以及主观环境舒适度评级。将生理信号与执行数据结合评估，揭示了客观性能指标未能反映的隐性生理成本。结果表明，所有环境条件下的任务表现保持稳定。由相位性皮肤电导水平（SCL）表征的自主神经负荷随温度升高而增加，而体力与认知负荷未受影响。感知环境舒适度与追踪误差或完成时间无显著关联。这些发现揭示了操作人员通过增加生理努力抑制热不适来维持稳定表现的代偿机制。该认知为开发生理感知型控制架构提供了依据，通过利用实时生理指标降低操作人员在非结构化环境中的工作负荷。