Chinese robot makers’ lament: if we only had a better ‘brain’, and more data
Chinese robotics companies are facing significant challenges in improving their products' interaction with the physical world, according to industry insiders at the World Artificial Intelligence Conference in Shanghai. The primary obstacles identified are a lack of sufficient data and a "brain" or advanced AI models.

Briefing Summary
AI-generatedChinese robotics companies are facing significant challenges in improving their products' interaction with the physical world, according to industry insiders at the World Artificial Intelligence Conference in Shanghai. The primary obstacles identified are a lack of sufficient data and a "brain" or advanced AI models. Experts emphasize the need to create a closed-loop iterative system that integrates hardware, data, models, and real-world scenarios. Currently, training data largely relies on human demonstrations, but effective translation into better embodied AI requires joint optimization of robot hardware, data collection, and physical structure. Limited robot deployment in real-world scenarios hinders the collection of diverse, multi-modal data, which is crucial for training "world models" that enable robots to understand and navigate their environment.
Article analysis
Model · rule-basedKey claims
4 extractedThe critical challenge for embodied AI is linking hardware, data, models, and real-world scenarios into a closed-loop iterative system.
The amount of multi-modal data for the physical world is inadequate compared to data used for large language models.
Robot deployment in real-world scenarios for data collection is too limited to effectively train embodied AIs.
Chinese robotics companies lack sufficient data and advanced AI 'brains' for better physical world interaction.