智能轮椅人机交互中的运动变异性与用户体验
Locomotion Variability and User Experience in Smart Wheelchair Human-Robot Interaction
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中文总结 AI 辅助
该研究在智能轮椅共享控制场景中，提出保留自然运动结构的支持自主辅助策略，发现其在任务表现相当的情况下，能提升用户感知自主感与有用性，凸显设计辅助机器人需尊重人类运动结构的重要性。
中文摘要 AI 辅助
人类运动具有内在变异性，且这种变异性与任务相关性相关：在任务关键节点的运动通常更一致，其他节点则更灵活。然而在人机交互（HRI）中，基于模型的辅助策略通常假设人类行为是确定性的，并抑制这种变异性，这可能改变交互体验并降低自主感。尽管运动变异性的功能意义日益受到重视，但在辅助交互中刻意保留这种变异性及其对用户体验的影响仍未得到充分探索。本文在共享控制场景中，实证研究不同辅助策略如何影响人类运动变异性、任务表现和主观交互体验。我们提出一种支持自主的共享控制策略，可保留用户的自然运动结构。该策略在用户研究中接受评估，研究中参与者在三种条件下操控智能电动轮椅：无辅助、传统的变异性降低辅助、变异性保留辅助。结果显示，辅助模式间与任务相关的表现相当，而保留自然运动变异性带来更优的交互体验：与传统辅助相比，参与者报告的感知自主感显著更高，且感知有用性最高。这些发现表明，感知变异性的辅助可在物理人机协作中同时支持表现和用户自主；更广泛地说，结果强调设计辅助机器人系统时，应尊重人类运动的具身结构，而非将变异性视为需忽略或消除的噪声。
英文摘要
Human movement is inherently variable, with variability structured according to task relevance: movements are typically more consistent at task-critical points and more flexible elsewhere. In human-robot interaction (HRI), however, model-based assistance strategies commonly assume deterministic human behavior and suppress such variability, potentially altering how interactions are experienced and lowering sense of agency. While movement variability is increasingly recognized as functionally meaningful, its deliberate preservation in assisted interaction, and its consequences for user experience, remain underexplored. In this paper, we empirically investigate how different assistance strategies shape human movement variability, task performance, and subjective interaction experience in a shared control setting. We introduce an autonomy-supportive shared control strategy that preserves users' natural movement structure. This approach is evaluated in a user study in which participants push an intelligent powered wheelchair under three conditions: no assistance, conventional variability-reducing assistance, and variability-preserving assistance. While task-relevant performance remained comparable across assisted modes, preserving natural movement variability led to more favorable interaction experiences. In particular, participants reported significantly higher perceived agency compared to conventional assistance and highest perceived usefulness. These findings suggest that variability-aware assistance can support both performance and user autonomy in physical human-robot collaboration. More broadly, the results highlight the importance of designing assistive robotic systems that respect the embodied structure of human movement rather than treating variability as noise to be neglected or eliminated.
发表机构
- Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
- Université de Sherbrooke（谢布鲁克大学）
机构由 AI 辅助整理，请以论文原文为准。
