We propose a framework for vision-based human pose estimation and motion prediction that gives conformal prediction guarantees for certifiably safe human-robot collaboration. Our framework combines aleatoric uncertainty estimation with OOD detection for high probabilistic confidence. To integrate our pipeline in certifiable safety frameworks, we propose conformal prediction sets for human motion predictions with high, valid confidence. We evaluate our pipeline on recorded human motion data and a real-world human-robot collaboration setting.

翻译：我们提出了一种基于视觉的人体姿态估计与运动预测框架，该框架为可认证安全的人机协作提供了保形预测保证。该方法将偶然不确定性估计与分布外检测相结合，以实现高概率置信度。为将我们的流程集成到可认证的安全框架中，我们提出了针对人体运动预测的保形预测集，该预测集具有高且有效的置信度。我们在记录的人体运动数据以及真实世界的人机协作场景中评估了该方法的性能。