基于视觉语言模型的早期动作预测在人机交互中的决策感知不确定性评估
Decision-Aware Uncertainty Evaluation of Vision-Language Model-Based Early Action Anticipation for Human-Robot Interaction
AI总结：
本文提出了一种系统评估基于视觉语言模型的短期动作识别在人机交互中的不确定性方法，通过引入时间前缀评估协议和校准度量标准，揭示了部分观测下的误校准模式和失败模式，为信心门控的人机交互模块提供了可靠性证据。
摘要：
共享工作空间中的机器人必须从部分、模糊的观测中解读人类动作，过度自信的早期预测可能导致不安全或干扰性的交互。在以自身为中心的视角下，视角变化和遮挡会增加感知噪声和模糊性。因此，下游的人机交互模块不仅需要一个动作假设，还需要在部分观测下的可信信心估计。最近基于视觉语言模型的方法由于其开放词汇和上下文感知推理能力被提出用于短期动作识别，但它们在时间前缀领域的不确定性可靠性仍鲜有研究。我们首次系统地评估了基于视觉语言模型的短期动作识别在人机交互中的不确定性。我们引入了时间前缀评估协议和校准及选择性预测的度量标准。我们还表征了在部分观测下存在的误校准模式和失败模式。我们的研究提供了使用视觉语言模型预测进行信心门控的人机交互模块所需的缺失可靠性证据。
英文摘要：
Robots in shared workspaces must interpret human actions from partial, ambiguous observations, where overconfident early predictions can lead to unsafe or disruptive interaction. This challenge is amplified in egocentric views, where viewpoint changes and occlusions increase perceptual noise and ambiguity. As a result, downstream human-robot interaction modules require not only an action hypothesis but also a trustworthy estimate of confidence under partial observation. Recent vision-language model-based approaches have been proposed for short-term action recognition due to their open-vocabulary and context-aware reasoning, but their uncertainty reliability in the temporal-prefix regime is largely uncharacterized. We present the first systematic evaluation of uncertainty in vision-language model-based short-term action recognition for human-robot interaction. We introduce a temporal-prefix evaluation protocol and metrics for calibration and selective prediction. We also characterize miscalibration patterns and failure modes under partial observations. Our study provides the missing reliability evidence needed to use vision-language model predictions in confidence-gated human-robot interaction modules.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
