State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China.
Sensors (Basel). 2020 Feb 10;20(3):939. doi: 10.3390/s20030939.
Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that deep reinforcement learning is a powerful technique for fixed-base manipulation tasks, most of them are not applicable to mobile manipulation. This paper investigates how to leverage deep reinforcement learning to tackle whole-body mobile manipulation tasks in unstructured environments using only on-board sensors. A novel mobile manipulation system which integrates the state-of-the-art deep reinforcement learning algorithms with visual perception is proposed. It has an efficient framework decoupling visual perception from the deep reinforcement learning control, which enables its generalization from simulation training to real-world testing. Extensive simulation and experiment results show that the proposed mobile manipulation system is able to grasp different types of objects autonomously in various simulation and real-world scenarios, verifying the effectiveness of the proposed mobile manipulation system.
移动操作在机器人技术中有广泛的应用。然而，由于移动基座和操作器的复杂协调，它通常比固定基座操作更具挑战性。尽管最近的工作已经证明深度强化学习是固定基座操作任务的强大技术，但它们大多数不适用于移动操作。本文研究如何利用深度强化学习来解决仅使用板载传感器的非结构化环境中的整体移动操作任务。提出了一种新颖的移动操作系统，该系统将最先进的深度强化学习算法与视觉感知相结合。它具有一种有效的框架，将视觉感知与深度强化学习控制解耦，从而使其能够从模拟训练推广到实际测试。广泛的仿真和实验结果表明，所提出的移动操作系统能够在各种仿真和真实场景中自主地抓取不同类型的物体，验证了所提出的移动操作系统的有效性。
