Core Insights - The article discusses the challenges in end-to-end robot learning, particularly focusing on the "Sim2Real" gap, which is primarily caused by the inadequacy of simulation environments to accurately replicate real-world scenarios [1][6][10]. Group 1: Challenges in Robot Simulation - Current simulation environments struggle with three main issues: insufficient realism in replicating real-world scenarios, high costs in scene asset acquisition and system configuration, and time-consuming data collection processes [1][5]. - The core obstacle is the performance drop during the Sim2Real transfer, which stems from the fundamental differences between simulated and real-world environments, such as object appearance, lighting effects, and spatial geometry [1][6]. Group 2: Existing Simulation Frameworks - Various simulation frameworks have been developed, but none meet the three critical requirements: high visual fidelity, accurate physical interaction, and efficient parallel scalability [3][6]. - Traditional simulators often compromise on either visual realism or physical accuracy, leading to ineffective training for robots [6][7]. Group 3: DISCOVERSE Framework - DISCOVERSE is an open-source simulation framework developed by Tsinghua University in collaboration with other institutions, integrating 3D Gaussian Splatting (3DGS), MuJoCo physics engine, and control interfaces into a unified architecture [5][10]. - The framework aims to bridge the Sim2Real gap by enhancing the realism of simulations through a three-layer innovation approach, focusing on accurate digital representation of real-world scenes and objects [10][12]. Group 4: Performance and Efficiency - DISCOVERSE significantly improves simulation speed, achieving rendering speeds up to 650 FPS on high-end hardware, which is three times faster than competing solutions [19][20]. - The framework supports a wide range of asset formats and robot models, enhancing compatibility and reducing the need for extensive configuration [21][22]. Group 5: Testing and Results - In comparative tests, DISCOVERSE outperformed other mainstream simulators in zero-shot transfer success rates across various tasks, demonstrating its effectiveness in real-world applications [24][27]. - The framework also enhances data collection efficiency, reducing the time required to gather demonstration data from 146 minutes to just 1.5 minutes, thus accelerating algorithm iteration [29]. Group 6: Future Implications - DISCOVERSE is positioned as a versatile robot simulation framework capable of supporting various complex tasks, with potential applications in robotics, drones, and autonomous driving sensors [30]. - The release of the framework's code and API aims to facilitate adoption by developers and enterprises, marking a significant milestone in the robotics industry [30].