自动驾驶领域，开环是基于离线数据的静态回放，算法决策与环境状态解耦，无法改变既定历史；闭 环则是在动态仿真中，车辆的每一次操作都会与环境产生交互，并实时改变后续的时空轨迹。 现有的世界模型输入一个明显不安全的一系列动作（例如冲向行人或驶出路面）时，模型为了维持生 成的连贯性，往往会产生幻觉，它会让行人凭空消失，或者让草地瞬间变成柏油路，从而强行生成一 个安全的未来。 2025年11月25日理想发布AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models 这篇论文核心解决的问题是: 如何通过闭环强化学习提升端到端自动驾驶的安全性与鲁棒性，特别是 解决现有世界模型无法正确预测危险后果的系统性缺陷。 鲁棒性指系统在面对输入扰动、参数不确定性或环境变化时，仍能维持性能稳定的能力。在自动驾驶 领域，指不仅要在设计运行域（ODD）内的标准场景中表现优异，也要在未知的、极端复杂的长尾 场景中保持决策的安全性和可靠性。 模仿学习核心两个问题: 1.分布偏移现实世界中充满了训练数 ...

Core Insights - The article discusses the inaugural "CMG World Robot Competition Series" featuring humanoid robots in combat, showcasing advancements in motion control and balance capabilities [2][5]. Group 1: Event Overview - The competition is the first of its kind globally, focusing on humanoid robots as the main participants in combat sports [2]. - The event featured four teams controlling the Yushu G1 humanoid robot, which stands 1.3 meters tall and weighs 35 kilograms, demonstrating 29 degrees of freedom [5]. Group 2: Technology and Control - The competition primarily utilized remote control technology, emphasizing the operator's reaction time alongside the robot's algorithms [3][10]. - Current remote control technology is likened to the robot's "small brain," while non-remote control technology, which requires advanced capabilities like visual recognition and real-time decision-making, is compared to the "big brain" [3][11]. Group 3: Performance Metrics - The competition employed a scoring system based on effective strikes, with different points awarded for hits to various body parts [5]. - The ability of robots to recover from falls within 8 seconds was a critical performance metric, testing both hardware and software resilience [8][9]. Group 4: Robustness and Material - "Robustness" is highlighted as a key performance indicator, referring to the robot's ability to maintain stability and performance under various disturbances [6][7]. - The robots are constructed using lightweight materials like carbon fiber and aluminum alloys, enhancing strength while reducing weight [9]. Group 5: Future Developments - Experts predict that achieving fully autonomous control in complex scenarios may take an additional 3 to 5 years, with significant challenges remaining in real-time perception and decision-making algorithms [4][14]. - The development of advanced hardware, such as high-precision sensors and AI chips, is essential for the evolution of non-remote control capabilities, but these components significantly increase costs [13].