当 ChatGPT 重构人类与文字的交互逻辑时，英伟达 CEO 黄仁勋在 CES 2026 的舞台上抛出了一个更具颠覆性的判断："物理 AI 的 ChatGPT 时刻已到来 —— 机器开始理解、推理并在真实世界中行动。" 这场近一个半小时的演讲里，名为 "Alpamayo" 的自动驾驶 AI 系统成为绝对主角，它不仅是英伟达在 智驾领域的又一次技术跃迁，更标志着自动驾驶从 "数据驱动" 向 "推理驱动" 的关键转折。 从 "被动响应" 到 "主动思考"，Alpamayo 破解自动驾驶 "长尾死结" 在自动驾驶行业，"长尾问题" 始终是悬在所有玩家头顶的达摩克利斯之剑 ——99% 的常规路况可通过数据训练覆盖，但剩下 1% 的罕见场景（如交通信 号灯故障、突发横穿马路的动物、极端天气下的路面结冰），却可能成为安全事故的导火索。过去，行业的解决方案是 "堆数据"，试图用百万甚至亿级 公里的路测数据覆盖所有可能性，但这不仅成本高昂，更无法应对 "从未出现过的场景"。 Alpamayo 的出现，恰恰提供了另一条路径。作为业界首个思维链推理 VLA（视觉 - 语言 - 动作）模型，它的核心突破在于让自动驾驶系统拥有了 ...

Summary of Conference Call on Robotics Industry Industry Overview - The humanoid robotics commercialization is still in its early stages, primarily applied in standardized processes within industrial settings, such as material handling in automotive manufacturing, but the actual usable scenarios are limited. Future applications are expected to emerge in standardized processes with high labor costs in hazardous environments [1][4] Key Points and Arguments - **Challenges in Commercialization**: Humanoid robotics face dual challenges in hardware and software. Hardware improvements are needed in actuator precision, sensor accuracy, power density, and battery life. Software improvements are required in human-machine interaction efficiency, multi-modal perception accuracy, visual image processing, and motion control stability [1][5] - **Data Collection Solutions**: To address the scarcity of training datasets, solutions include increasing real data collection (e.g., Zhiyuan's simulated living spaces) and employing physical simulation methods (e.g., NVIDIA's techniques) to enhance data quality and accelerate commercial application expansion [1][6][7] - **Training Data Efficiency**: By adjusting scene parameters or modifying scenarios, a small amount of real-world interaction data can generate hundreds to thousands of data points, significantly improving data acquisition efficiency and reducing costs. The future mainstream approach may combine real data collection with simulated data generation [1][8] - **Trends in Robotics Models**: The development of large models for robotics is trending towards multi-system architectures, such as NVIDIA's Grace Hopper. Future models need to address multi-modal and generalization capabilities, enabling robots to understand visual, linguistic, auditory, and tactile information [1][9] Additional Important Insights - **Technological Progress**: In the past two to three years, significant technological advancements have been observed in the humanoid robotics sector, with companies like UBTECH demonstrating impressive motion capabilities. However, humanoid robots still struggle with executing simple yet complex tasks, indicating that their intelligence level has not yet reached a fluid stage [2] - **Communication Protocols**: The EtherCAT protocol, with its distributed architecture, controls communication latency at the microsecond level, outperforming traditional CAN bus protocols and other real-time industrial Ethernet protocols, positioning it as a potential mainstream communication protocol for robotics [3][12] - **Market Developments**: DRECOM is set to release a new NPU and DMC stacked packaging product, suitable for running large models on the edge, expected to enter the market by 2025 or 2026. This indicates a growing focus on automation and data collection in investment trends [1][14] - **Sensor Technology**: The development direction for mechanical and tactile sensing is towards more precise perception and execution, enabling robots to understand real-world information accurately and perform fine operations [1][11] - **Chip Applications**: The current landscape for edge chips in robotics includes high-performance models from NVIDIA and Tesla for complex tasks, while domestic chips are being utilized for less demanding functions, indicating a growing opportunity for domestic chip performance enhancement [1][13]