人类机器人任务最小干涉人类机器人任务的等级人类运动预测和逻辑-地质构造规划 (Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks)

In this paper, we tackle the problem of human-robot coordination in sequences of manipulation tasks. Our approach integrates hierarchical human motion prediction with Task and Motion Planning (TAMP). We first devise a hierarchical motion prediction approach by combining Inverse Reinforcement Learning and short-term motion prediction using a Recurrent Neural Network. In a second step, we propose a dynamic version of the TAMP algorithm Logic- Geometric Programming (LGP). Our version of Dynamic LGP, replans periodically to handle the mismatch between the human motion prediction and the actual human behavior. We assess the efficacy of the approach by training the prediction algorithms and testing the framework on the publicly available MoGaze dataset

翻译：在本文中,我们处理操纵任务序列中的人体-机器人协调问题。我们的方法将等级人类运动预测与任务和动作规划(TAMP)相结合。我们首先设计等级运动预测方法,利用经常性神经网络将反强化学习和短期运动预测结合起来。在第二步,我们提出一个动态版本的TAMP算法逻辑-几何规划(LGP)。我们版本的动态LGP,定期调整计划,以处理人类运动预测与实际人类行为之间的不匹配。我们通过培训预测算法和测试可公开获得的MOGaze数据集的框架,评估该方法的效力。