从“能动”到“灵动”，机器人智能化步入新篇章

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]