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.

翻译：在协作式人机任务中识别意图能够提升团队表现及人类对机器人的感知。在存在错误的物理动态任务中，意图可能与观察到的结果存在差异。我们创建了包含10名参与者1227次投球动作的数据集，观察到47%的投掷存在错误，其中16%完全偏离目标。本研究利用捕捉参与者对投掷结果反应的面部图像，预测投掷结果是否为错误状态，进而判定实际投掷意图。我们提出的结果预测方法在正面视角视频上的表现，较先前用于此任务的双流架构提升38%。此外，我们提出一种一维卷积神经网络模型，结合从错误频率中学习到的先验知识，为该投掷任务构建了端到端的结果与意图识别流程。