Moving a human body or a large and bulky object can require the strength of whole arm manipulation (WAM). This type of manipulation places the load on the robot's arms and relies on global properties of the interaction to succeed---rather than local contacts such as grasping or non-prehensile pushing. In this paper, we learn to generate motions that enable WAM for holding and transporting of humans in certain rescue or patient care scenarios. We model the task as a reinforcement learning problem in order to provide a behavior that can directly respond to external perturbation and human motion. For this, we represent global properties of the robot-human interaction with topology-based coordinates that are computed from arm and torso positions. These coordinates also allow transferring the learned policy to other body shapes and sizes. For training and evaluation, we simulate a dynamic sea rescue scenario and show in quantitative experiments that the policy can solve unseen scenarios with differently-shaped humans, floating humans, or with perception noise. Our qualitative experiments show the subsequent transporting after holding is achieved and we demonstrate that the policy can be directly transferred to a real world setting.

本文利用基于拓扑的坐标将任务建模为强化学习问题，以直接响应外部干扰和人体动作的行为方式，学习生成运动，解决某些救援或病人护理场景中的大型物品运输。仿真动态海上救援场景并进行定量实验，展示学习策略可以解决不同形状的人类，漂浮的人类或感知噪声。我们的定性实验展示了持续保持后的运输，证明了该策略可以直接转移到实际场景中。

基于拓扑表示的强化学习在带整臂操作的人体运动中的应用