论文标题
使用强大的关键点表示机器人操纵的端到端强化学习
End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation
论文作者
论文摘要
我们使用强大而有效的关键点表示，为机器人操纵任务提供了端到端的增强学习（RL）框架。所提出的方法通过自我监管的自动编码器体系结构从相机图像中学习作为状态表示的关键点。关键点编码几何信息，以及工具和目标的关系，以紧凑的表示，以确保有效而健壮的学习。在关键点学习之后，RL步骤然后从提取的关键点状态表示中学习机器人运动。关键点和RL学习过程完全在模拟环境中完成。在不同情况下，我们证明了提出方法对机器人操纵任务（包括抓握和推动）的有效性。我们还研究了受过训练的模型的概括能力。除了强大的关键点表示外，我们还进一步应用了域随机化和对抗性训练示例，以实现现实世界机器人操纵任务中的零射击SIM到现实传递。
We present an end-to-end Reinforcement Learning(RL) framework for robotic manipulation tasks, using a robust and efficient keypoints representation. The proposed method learns keypoints from camera images as the state representation, through a self-supervised autoencoder architecture. The keypoints encode the geometric information, as well as the relationship of the tool and target in a compact representation to ensure efficient and robust learning. After keypoints learning, the RL step then learns the robot motion from the extracted keypoints state representation. The keypoints and RL learning processes are entirely done in the simulated environment. We demonstrate the effectiveness of the proposed method on robotic manipulation tasks including grasping and pushing, in different scenarios. We also investigate the generalization capability of the trained model. In addition to the robust keypoints representation, we further apply domain randomization and adversarial training examples to achieve zero-shot sim-to-real transfer in real-world robotic manipulation tasks.
