美航空展览惊现"红旗-16仿制品" FFFFFR: 轮胎都是充气的，意在炒作 据美国《新闻周刊》、比利时《陆军识别》网 站等媒体当地时间7月31日报道，美国威斯展 星州国民警卫队表示，美国正在使用中国防空 导弹系统仿制品，作为其战斗机飞行员训练的 音的分。 《陆军识别》网站称，这是一辆可牵引、高保真的红旗-16导弹发射车仿制品，使美国飞行员能够在真实的作战条件下演练探测、"威胁"识别并进行程序 优先级排序。 威斯康星州国民警卫队在7月30日发布的新闻稿中称，该仿制品是一种全面的训练辅助工具，旨在让第五代战斗机飞行员熟悉他们在战斗场景中可能遇到 的现代地空导弹武器系统。 美国"防务博客"网站在报道此事时分析称，过去，美军主要使用苏联和俄罗斯的武器模型进行目标训练，引入中国防空导弹系统的仿制品反映了美国国防 规划"更广泛的转变"，即其越来越重视中国军事防空系统。 对于美方展出的所谓"红旗-16仿制品"，《航空知识》主编、军事观察员王亚男在接受环球网记者采访时解读称，该模型更多是一个"模拟训练器材"，可 能为美军航空兵提供一个目视、光学识别、或者在红外条件下识别的一个参照。 "可以理解为美方做了一个模型，这样未来在 ...

Core Insights - The article discusses a new approach to Visual-Language-Action (VLA) models that leverages visual language models (VLMs) for efficient robot trajectory prediction, addressing high training costs and data limitations associated with traditional VLA systems [2][3]. Group 1: Introduction and Background - VLA models integrate visual, language, and interaction data to enable fine-grained perception and action generation, but face challenges such as high computational costs, data scarcity, and evaluation benchmarks [3]. - The proposed method utilizes controllable synthetic datasets for training lightweight VLA systems, which can be applied across various domains, particularly in robotics [3]. Group 2: Technical Methodology - The foundational model is based on the pre-trained VLM PaliGemma2, which predicts key poses of the robot's end effector from real-time images, robot states, and task descriptions [6]. - The system employs a single-step prediction approach to enhance training efficiency, focusing on predicting two key trajectory poses rather than full trajectories [6][8]. - The method extends to few-shot imitation learning, allowing the model to infer tasks from demonstration image-trajectory pairs without requiring fine-tuning on new scene images [8]. Group 3: Data Generation and Evaluation - The training dataset is generated using the ManiSkill simulator, which creates diverse environments and tasks, enhancing the model's ability to generalize to real-world scenarios [9][10]. - Real-world evaluation is conducted using the DROID dataset, which includes various scenes and actions, allowing for a comprehensive assessment of the model's performance [11]. Group 4: Experimental Results - Experiments demonstrate that incorporating depth information significantly improves simulation success rates and reduces failure cases [12]. - The model's performance is evaluated across different datasets, with success rates reported at 70% for the easy version and 28% for the hard version of the CLEVR dataset [16][17]. - The article highlights the importance of camera and scene randomization in achieving robustness in real-world applications [16]. Group 5: Inference Strategies - The article discusses the impact of input image cropping on performance, indicating that precise target localization is crucial for successful robot operations [18]. - Various decoding strategies are evaluated, with the proposed beam-search-NMS method outperforming traditional approaches in terms of accuracy and diversity of predicted trajectories [20][23].

Core Viewpoint - The article highlights the advancements in humanoid robots, particularly focusing on Figure's new model, which utilizes reinforcement learning to achieve more natural walking patterns, resembling human movement more closely [3][4][22]. Group 1: Technological Advancements - Figure's new humanoid robot, Figure 02, demonstrates significant improvements in walking, appearing more human-like with a lighter gait and faster speed [4][6]. - The walking control system is trained using reinforcement learning, which allows the robot to learn how to walk like a human through simulated trials [9][14]. - The training process involves high-fidelity physical simulations, enabling the collection of years' worth of data in just a few hours [10][14]. Group 2: Simulation Techniques - The training incorporates domain randomization and high-frequency torque feedback to bridge the gap between simulation and real-world application, allowing the learned strategies to be applied directly to physical robots without additional adjustments [11][18]. - The robots are exposed to various scenarios during training, learning to navigate different terrains and respond to disturbances [15][18]. Group 3: Future Plans and Industry Context - Figure plans to expand this technology to thousands of Figure robots, indicating a significant scaling of their operations [21]. - The article notes a broader trend in the industry, with many companies, including Vivo, launching their own robotics initiatives, reflecting a growing interest in humanoid robots [24][25].