sim2real挑战赛学习资料

以下是一些Sim2Real挑战赛的学习资料：

1. Sim-to-Real Reinforcement Learning for Robotics: A Reality Gap is a challenge in robotics where models trained in simulation often fail to perform well in the real world. This paper examines the problem and proposes a sim-to-real approach to reinforcement learning for robotics.

2. Sim2Real Viewpoint Invariant Visual Servoing by Recurrent Control: This paper proposes a view-invariant visual servoing technique that can generalize well from simulated to real-world environments.

3. Sim-to-Real Transfer of Robotic Control with Dynamics Randomization: This paper introduces a method of training robots in simulation using randomized dynamics and then transferring the learned control policies to the real world.

4. Sim-to-Real Transfer for Deep Reinforcement Learning with Safe Exploration: This paper proposes a method for safe exploration in Sim2Real transfer for deep reinforcement learning.

5. Sim2Real View-Invariant Visual Servoing by Combining Simulation and Deep Learning: This paper proposes a view-invariant visual servoing technique that combines simulation and deep learning to achieve robustness to viewpoint changes.

6. Sim2Real Transfer for Robotic Manipulation: A Survey: This paper provides a comprehensive survey of the existing literature on Sim2Real transfer for robotic manipulation.

7. OpenAI Robotics: Sim2Real Transfer: This blog post by OpenAI provides an overview of Sim2Real transfer for robotics and highlights some of the challenges and opportunities in the field.

8. NVIDIA Research: Sim-to-Real Transfer Learning for Robotics: This video by NVIDIA Research provides an overview of Sim2Real transfer learning for robotics and showcases some of the recent advancements in the field.

9. Sim-to-Real Transfer of Robotic Control with Deep Reinforcement Learning: This paper proposes a method for Sim2Real transfer of robotic control using deep reinforcement learning and demonstrates its effectiveness on a real-world robotic arm.

10. Sim-to-Real Transfer of Control Policies for Robotics using Adversarial Domain Adaptation: This paper proposes a method for Sim2Real transfer of control policies for robotics using adversarial domain adaptation and demonstrates its effectiveness on a real-world robotic arm.