Automatic robot activity understanding plays an important role in human–computer interaction (HCI), especially in smart home service robots. Existing manipulator control methods, such as position control, vision-based control method, fail to meet the requirements of autonomous learning. Reinforcement learning can cope with the interaction of robot control and environment; however, the method should relearn the control method when the position of target object changes. To solve this problem, this paper proposes a quality model to utilize deep reinforcement learning scheme to achieve an end-to-end manipulator control. Specifically, we design a policy search algorithm to achieve automatic learning of manipulator. To avoid relearning of manipulator, we design convolutional neural network control scheme to remain the robustness of manipulator. Extensive experiment has shown the effectiveness of our proposed method.

自动的机器人活动理解在人机交互（HCI）中起着重要作用，尤其是在智能家居服务机器人中。现有的机械手控制方法（例如位置控制，基于视觉的控制方法）无法满足自主学习的要求。强化学习可以应对机器人控制与环境的相互作用；但是，当目标对象的位置改变时，该方法应重新学习控制方法。为了解决这个问题，本文提出了一种质量模型，该模型利用深度强化学习方案来实现端到端机械手控制。具体来说，我们设计了一种策略搜索算法来实现机械手的自动学习。为了避免重新学习机械手，我们设计了卷积神经网络控制方案以保持机械手的鲁棒性。