论文标题
学习以目标为导向的非划分在混乱的场景中
Learning Goal-Oriented Non-Prehensile Pushing in Cluttered Scenes
论文作者
论文摘要
在杂乱无章的场景中推动物体是一项具有挑战性的任务，尤其是当要推动的对象最初具有未知的动态和触摸其他实体时，必须避免降低损害的风险。在本文中，我们通过应用深入的强化学习来解决此问题，以为在平面表面上作用的机器人操纵器的推动动作，在该机器人表面上必须将物体推到目标位置，同时避免在同一工作空间中的其他项目。通过从场景的深度图像和环境的其他观察结果中学到的潜在空间，例如末端效应器和对象之间的接触信息以及与目标的距离，我们的框架能够学习避免与其他物体碰撞的接触式推动行动。随着实验性的自由度机器人臂显示的结果，我们的系统能够成功地将物体从开始到结束位置推动，同时避免附近的物体。此外，我们评估了我们所学的政策与移动机器人的最先进的推动控制器相比，表明我们的代理在成功率，与其他对象的碰撞以及在各种情况下连续对象联系方面的性能更好。
Pushing objects through cluttered scenes is a challenging task, especially when the objects to be pushed have initially unknown dynamics and touching other entities has to be avoided to reduce the risk of damage. In this paper, we approach this problem by applying deep reinforcement learning to generate pushing actions for a robotic manipulator acting on a planar surface where objects have to be pushed to goal locations while avoiding other items in the same workspace. With the latent space learned from a depth image of the scene and other observations of the environment, such as contact information between the end effector and the object as well as distance to the goal, our framework is able to learn contact-rich pushing actions that avoid collisions with other objects. As the experimental results with a six degrees of freedom robotic arm show, our system is able to successfully push objects from start to end positions while avoiding nearby objects. Furthermore, we evaluate our learned policy in comparison to a state-of-the-art pushing controller for mobile robots and show that our agent performs better in terms of success rate, collisions with other objects, and continuous object contact in various scenarios.
