通过人脸反应识别投掷失误，还原人类真实意图。
That was not what I was aiming at! Differentiating human intent and outcome in a physically dynamic throwing task
- 用面部表情预测投掷结果是否出错
- 误判率降低38%，准确识别47%的失误
- 适合人机协作中理解人类真实意图
在人机协同任务中，识别人类意图可提升团队表现和对机器人的感知。在物理动态任务中，意图与实际结果常不一致。我们收集了10名参与者共1227次投掷数据，发现47%的投掷存在失误，其中16%完全未击中目标。研究利用投掷后的人脸图像捕捉反应，预测投掷结果是否为失误，并推断真实意图。所提方法在前向视频上比先前两流架构提升38%。此外，提出一维CNN模型结合失误频率先验，实现投掷结果与意图识别的端到端流程。
原文摘要 · Abstract (English)
Recognising intent in collaborative human robot tasks can improve team performance and human perception of robots. Intent can differ from the observed outcome in the presence of mistakes which are likely in physically dynamic tasks. We created a dataset of 1227 throws of a ball at a target from 10 participants and observed that 47% of throws were mistakes with 16% completely missing the target. Our research leverages facial images capturing the person's reaction to the outcome of a throw to predict when the resulting throw is a mistake and then we determine the actual intent of the throw. The approach we propose for outcome prediction performs 38% better than the two-stream architecture used previously for this task on front-on videos. In addition, we propose a 1-D CNN model which is used in conjunction with priors learned from the frequency of mistakes to provide an end-to-end pipeline for outcome and intent recognition in this throwing task.
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