Experts at WAIC discuss the bottlenecks preventing embodied AI from improving robots’ real-world interactions
Chinese robotics companies lack both sufficient data and a good “brain” to improve the interaction of their products with the physical world, according to industry insiders at the World Artificial Intelligence Conference (WAIC), which concluded on Monday in Shanghai.
The most critical challenge for the embodied AI industry was to “link hardware, data, models and real-world scenarios into a closed-loop iterative system”, said Wang Xiaogang, co-founder of SenseTime and chairman of its robotics spin-off Ace Robotics, in an interview with the South China Morning Post on Saturday.
Currently, a vast amount of training data is collected from human demonstrations. But for that data to effectively translate into better embodied AIs, hardware design for robots must be jointly optimised alongside data-collection methods and physical structure, according to Wang.
He added that robot deployment across real-world scenarios, where data originated, remained too limited.
“The key question is how to unlock these scenarios and replicate them at scale,” he said.
The amount of available multi-modal data about the physical world was far from adequate, compared with that used in large language models, according to Yao Maoqing, partner and senior vice-president of Shanghai-based humanoid robot maker AgiBot. This created one of the bottlenecks in the current training of the so-called world models that were expected to allow next-generation humanoid robots to model and navigate their surroundings, he said.