End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation
This work addresses robotic manipulation for automation, with incremental improvements in representation and transfer learning.
The authors tackled robotic manipulation by developing an end-to-end reinforcement learning framework that uses a self-supervised keypoints representation from camera images, achieving zero-shot sim-to-real transfer in tasks like grasping and pushing.
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.