Unitree's Wang Xingxing: 2027 Is Very Likely to Be the Tipping Point for the Robotics Industry
At the 2026 World Internet Conference Wuzhen Summit, Wang Xingxing, founder of Unitree Robotics, judged that 2027 is very likely to become the critical turning point for the robotics industry or the development of robot models. He pointed out that the current data utilization rate for robots is relatively low, with large amounts of data not being fully utilized, and that self-evolution of physical AI models is expected to improve data utilization efficiency. Wang Xingxing said that the world's top models are already very close to the "GPT moment" of embodied intelligence, but the alignment between AI model inputs and outputs and the real world still falls a bit short, and such problems are expected to be solved within the next one to three years.
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On November 4, at the "Internet Entrepreneurs Forum" of the 2026 World Internet Conference Wuzhen Summit, Wang Xingxing, founder of Unitree Robotics, spoke about technological breakthroughs in the robotics industry, saying that at present, including at Unitree, the utilization rate of robot data remains low, and a large amount of data is not fully utilized. He believes that as physical AI models achieve self-evolution, robots are expected to improve data utilization efficiency and accelerate technological iteration.
"For us, to produce and manufacture, deploy a thousand or ten thousand robots to test and collect data is, in a sense, a very low-cost and relatively easy thing." Wang Xingxing said that at present the company's robot data utilization rate is somewhat low, "a large amount of data is wasted, and the AI models have not been put to good use."
But this is still relatively rare in the robotics field. He said the logic is to have AI models write code and train in a simulation environment, then deploy them to robots for testing, and after testing is complete, humans or robots can give scores. Then, these results are fed back to the AI models so they can revise the programs and train again.
In this process, what value do humans mainly play?
"Up to now, I think the greatest value of humans lies in 'scoring,' evaluating whether the AI model is doing well, or setting some goals for the AI to achieve." Wang Xingxing also mentioned that as AI models become increasingly powerful, they will also learn human evaluation metrics and preferences. At present, in the video model field, this effect is relatively intuitive. Wang Xingxing believes that as models self-evolve and iterate, their data utilization rate will also be much higher.
As for the timing of breakthroughs in the robotics industry, Wang Xingxing gave a relatively optimistic judgment. He said that 2027 has a high probability of becoming a critical point for the robotics industry or the development of robot models.
Wang Xingxing mentioned that the "GPT moment" of embodied intelligence is when robots can complete about 80% of tasks through voice commands in about 80% of unfamiliar scenarios. This time he said that the world's top models are already very close to this goal, but key technical obstacles remain.
He called this obstacle "the problem of the last few millimeters."
He explained that at present, top multimodal models already have strong spatial understanding and task planning capabilities. After sufficient training, robots can achieve a success rate close to 100% when performing a single task at a fixed workstation, but operational efficiency still needs to be improved. For tasks such as grasping and carrying, models can also achieve relatively high success rates, but in scenarios requiring fine operations, such as disassembling and assembling mobile phones or threading a needle, the success rate is still relatively low.
"AI models do not do a particularly good job in the part involving contact with the physical world, that is, the alignment between the input and output of AI models and the real world is still a bit off." Wang Xingxing believes that robots still lack sufficiently accurate understanding of information such as contact and touch with the physical world, and this is precisely the key to completing complex operations. He expects that such problems are expected to be solved in the next one to three years.
However, these judgments by Wang Xingxing are still predictions about technological progress. A high task success rate at a fixed workstation does not mean that robots already have the general capability to work across scenarios, and the mechanism of model self-evolution still needs further verification.
Beyond expectations for technological breakthroughs, Unitree's performance in the capital market has also attracted attention. On November 4, the stock price of Unitree Robotics (688836.SH) rose by more than 8% at one point during intraday trading, surpassing Yuanjie Technology and ranking fourth among A-share stock prices.
For this company, which has already achieved large-scale robot manufacturing, competition in the next stage lies not only in how many robots it can produce, but also in whether it can enable models to make full use of data, so that robots can move from completing specific actions to autonomously completing tasks in more unfamiliar scenarios.
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