Lai Jie, the First Employee of Tencent's Robotics X Lab: This AI Wave Is an Intellectual Revolution, Not a Robot Revolution
Lai Jie, founder of Astribot, argues that companies like Unitree have proven robots' physical capabilities are already strong enough; the current bottleneck is AI's lack of concrete understanding of the physical world—specifically, missing concepts of size and space. He advocates redesigning robot bodies around AI learning needs, using cable-driven mechanisms to achieve high force transparency, positioning robots as spatial data collectors and application terminals for large models, and predicts that robots may enter households next year.
When Lai Jie left Tencent in late 2022 to found Astribot, ChatGPT had just been released days earlier. However, his technical foresight went even further back: while conducting data-driven research at Tencent’s Robotics X laboratory, he observed that Transformers were evolving from language to multimodal processing. He reasoned that if Token design was reasonable, all modalities could be aligned in the same space—a crucial step for data-driven robotics.
His core judgment now is that this wave of AI is more of an intellectual revolution than a robot revolution. Companies like Unitree have already demonstrated that robotic physical capabilities can be made sufficiently strong; the next challenge is enhancing intelligence. This implies that robot bodies should be repositioned as learning tools for AI—serving simultaneously as data collectors and application terminals.
This positioning rests on both evolutionary reasoning and experimental evidence. Lai Jie derives design principles from biological evolution: humans have five fingers, and eagles have four toes (two front, two back), primarily because they grasp different things, and what they grasp is determined by their brains. In other words, intelligence determines bodily morphology, not the other way around. A Brief History of Intelligence notes that the emergence of the opposable thumb was a key physical node in human intelligent evolution; only with this change could humans adapt to various tools.
Another piece of evidence comes from his experience at the Tencent lab. They trained industrial robots like KUKA and UR using bartender data, but later purchased an ABB robotic arm separately—not for its industrial reliability, but because its structure resembled a human’s, with degrees of freedom and joints close to those of the human body. The result was that models trained on the ABB arm performed better. Lai Jie concluded that when a robot’s structure approximates the human form, the transfer gap from human data is minimized, leading to better model performance.
Astribot names this philosophy DFAI (Design for AI). Regarding hardware selection, they adopted a cable-driven solution—using high-strength ropes to transmit power, moving motors from the joints to the base. Lai Jie explained that the initial choice of cable drive was not to mimic human tendons, but to find a transmission method with the best force transparency.
What is force transparency? Lai Jie uses a daily example: when a person shakes hands with a robot, either you are forcing it or it is forcing you, with no sense of mutual responsiveness. Many robots, when placing a coffee cup down, release their grip as soon as they judge the cup is near the table, causing it to drop with a clatter. Humans do not act this way; we release our grip only when we feel the table supporting the cup and our hands no longer need to exert force. Cable drive allows robots to perceive changes in end-effector forces, enabling them to learn this tactile-feedback-based intelligent relationship rather than relying on absolute position judgments.
This directly connects to the current data bottleneck in embodied AI. Lai Jie believes that native multimodal large models (which no longer separate LLMs and ViTs) have solved robots’ abstract understanding of the physical environment, but concrete understanding remains missing—internet data and existing collected data lack concepts of size and spatial awareness, possessing only visual awareness. The solution is to provide large models with complete spatial-concrete information through the robot itself, forming a data flywheel.
Lai Jie predicts this flywheel has started turning this year, as GPT also begins training first-person perspective (EGO) data and robot data. He explicitly stated that it is very likely robots will enter households next year.
Regarding the relationship between embodied models and foundational large models, Lai Jie uses a cooking metaphor: You video call your mother, and she guides you to add oil, but you must know how to perform the action yourself, because she cannot pour the oil for you. The embodied model is that part which 'knows how to pour the oil'—it perceives physical parameters (weight, force, grip stability) and uses natural language to ask GPT for solutions when uncertain. He opposes training all tactile information into the foundational large model, arguing it is cost-inefficient and would create communication bandwidth issues.
Lai Jie firmly opposes robots entering factories to screw bolts. His reason is that a robot’s greatest advantage lies in its broad capability, which cannot be confined to a single domain. Just as large models can answer questions on medicine, management, history, and science, robots entering homes should not be limited to single tasks. On industrial cost considerations, he personally prefers using fixed programs for repetitive tasks, noting that bosses might not be willing to pay token fees for every single action.
However, Lai Jie admits that Astribot is far from reaching the level of the iPhone 4. He maps their S1 model to the Apple I, and the T1 to the Apple II, stating they have not yet reached the Macintosh stage. But he believes industry development speed exceeds expectations, and blockbuster robot products at the Macintosh or iPhone 4 level will appear next year.
It must be emphasized that all of the above represents strategic judgments and first-party narratives from Lai Jie as the founder of Astribot. The cost advantages of cable drive, the intelligence gains from force transparency, and the prediction of household entry next year have not been independently verified. His DFAI concept offers a route for embodied intelligence distinct from 'build a strong body first, then install AI,' but whether this route can be realized in engineering and commerce depends on product iterations and market feedback that are not yet public.