成本相差200倍！遥操作、仿真、UMI、视频学习，谁才是具身智能数据领跑者？

Core Insights - The investment and financing heat in the embodied intelligence industry continues to rise, with a consensus that data collection is a critical breakthrough for advancing from L1 specific task intelligence to L2 combined task intelligence and beyond [1][4] Data Collection as a Key Variable - The core goal of embodied intelligence is to enable robots to possess common sense understanding, allowing them to deduce operational logic based on past experiences when faced with unfamiliar objects and tasks, which relies on high-quality, multi-modal interaction data [4][6] - Achieving human-eye-level 3D perception requires constructing a dataset of over 1 billion entries, highlighting the industry's urgent need for efficient and high-quality data collection solutions [3][6] Current Development Stage - Leading domestic companies are still in the early L1 development stage, capable of performing single-position tasks in specific environments, while the π0.5 model has achieved over 60% accuracy in long-range tasks in real home environments, nearing L2 levels [6][12] - The pre-training effect is crucial for the advancement of embodied intelligence technology, directly dependent on the "quantity" and "quality" of data [6][12] Four Core Data Collection Solutions - The selection of data collection solutions in embodied intelligence is fundamentally about balancing "cost, precision, and generalization capability" [7][28] - Remote Operation: High precision but high cost, with a complete setup exceeding 200,000 yuan, making it a significant financial burden [8][12] - Simulation: Low cost but suffers from distribution shift issues, making it less effective in real-world applications [14][16] - UMI Multi-modal Sensor Fusion: A cost-effective choice for SMEs, providing a balance between cost and precision, but limited in full-body motion capture capabilities [19][21] - Video Learning: Led by Tesla, this low-cost exploration method captures employee task execution videos, significantly reducing costs compared to remote operation [22][24] Industry Trends and Future Directions - The future trend in data collection for embodied intelligence will likely involve the integration of multiple solutions to achieve a balance of cost, precision, and scale [28] - The ultimate goal is to achieve an "autonomous data loop," where robots can independently complete tasks, collect data, and optimize models without human intervention [28]