Over the past year, I have watched the humanoid robot move from research prototypes into real factory floors, outdoor staircases, snowy fields, and multi-robot collaboration scenarios. The industry dynamics around us have changed dramatically. What once seemed like a remote dream is now an engineering reality. In this article, I want to share my first-person perspective on the latest upgrades and group intelligence breakthroughs that are reshaping the humanoid robot landscape.
I have spent countless hours observing our full-sized humanoid robot pushing its limits. The latest upgrade gave the humanoid robot vision-based perception for walking, the ability to climb complex stair terrain, and the capacity to run at high speed on difficult ground. At the same time, in another side of our work, dozens of humanoid robot units were connected through a group brain network in a 5G smart factory, performing collaborative sorting, collaborative handling, and precise assembly. These two strands of progress are not separate; they both depend on the same fundamental principle: the humanoid robot must tightly integrate perception, cognition, action, and learning.
I believe the humanoid robot is entering a new era. In the past, most robots were pre-programmed for fixed tasks. Now, the humanoid robot can perceive its environment, reason about complex goals, coordinate with other robots, and adapt its body to changing terrain. This is the essence of embodied intelligence. For me, the most exciting part is that the humanoid robot is no longer limited by a single script. It has a brain, a cerebellum, a body, and a social communication layer.
Embodied intelligence core. At the center of our design is an embodied intelligence loop. The humanoid robot is modeled as a floating-base rigid-body system with multiple degrees of freedom. We define the generalized coordinate vector as:
$$
\mathbf{q} = [\mathbf{p}_{base}^\top, \boldsymbol{\theta}_{base}^\top, \mathbf{q}_{joint}^\top]^\top \in \mathbb{R}^{6+n}
$$
where \(\mathbf{p}_{base}\) is the base position, \(\boldsymbol{\theta}_{base}\) is the base orientation, and \(\mathbf{q}_{joint}\) contains all joint angles. For a humanoid robot with \(n\) actuated joints, this vector fully describes the configuration of the machine. The dynamics of the humanoid robot follow the standard rigid-body equation:
$$
M(\mathbf{q})\ddot{\mathbf{q}} + C(\mathbf{q},\dot{\mathbf{q}})\dot{\mathbf{q}} + G(\mathbf{q}) = \boldsymbol{\tau} + J_c^\top \mathbf{F}_c
$$
Here \(M(\mathbf{q})\) is the mass matrix, \(C(\mathbf{q},\dot{\mathbf{q}})\) captures Coriolis and centrifugal effects, \(G(\mathbf{q})\) is the gravity vector, \(\boldsymbol{\tau}\) is the joint torque vector, \(J_c\) is the contact Jacobian, and \(\mathbf{F}_c\) is the contact force vector. For the humanoid robot, contact constraints are extremely important. The feet and sometimes hands interact with the environment. The contact constraints can be written as:
$$
J_c \ddot{\mathbf{q}} + \dot{J}_c \dot{\mathbf{q}} = 0
$$
This equation must be satisfied whenever a foot of the humanoid robot is in contact with the ground. The whole-body controller then solves an optimization problem at every time step. The humanoid robot must find joint torques that track desired foot placements, maintain balance, and respect actuator limits. This is the low-level foundation of everything described in this article.
Decision-making as learning. At the higher level, the humanoid robot is an agent that learns how to act. We formulate the decision-making problem as a Markov decision process:
$$
\pi^* = \arg\max_{\pi} \mathbb{E}_{(s,a)\sim \rho_\pi}\left[ \sum_{t=0}^{\infty} \gamma^t r(s_t,a_t) \right]
$$
where \(s_t\) is the state, \(a_t\) is the action, \(r\) is the reward, \(\gamma\) is the discount factor, and \(\rho_\pi\) is the state-action distribution induced by policy \(\pi\). In this framework, the humanoid robot can learn from experience rather than relying on hard-coded commands. This is especially valuable for complex terrains and multi-robot cooperation.
The humanoid robot also needs a rich sensory system. Visual cameras, inertial measurement units, joint encoders, force sensors, and tactile sensors feed information into the control loop. We can summarize the sensorimotor policy of the humanoid robot as:
$$
a_t = \pi_\theta(z_t, c_t, g_t)
$$
where \(z_t\) is the visual feature extracted from camera images, \(c_t\) is the proprioceptive state from joint sensors and inertial sensors, and \(g_t\) is the goal. The parameters \(\theta\) are learned through deep reinforcement learning and imitation learning. This formulation is the basis of the new perception-based walking capability.
Perception-based walking. One of the most important breakthroughs of the upgraded humanoid robot is vision-based perception during walking. Previously, many humanoid robot systems used a fixed gait pattern or assumed that the floor was flat. Now, the humanoid robot can look ahead, recognize the terrain, estimate ground height, and adjust its gait in real time.
During walking, the humanoid robot extracts terrain features from images:
$$
z_t = \phi_{vision}(I_t), \quad c_t = \phi_{proprio}(q_t, \dot{q}_t, \omega_t)
$$
where \(I_t\) is the camera image, \(\omega_t\) is the angular velocity from the inertial measurement unit, and \(\phi_{vision}\) and \(\phi_{proprio}\) are neural networks. The humanoid robot then builds a terrain model:
$$
\mathcal{T} = \{ \mathbf{p}_s, \mathbf{n}_s, \phi_r, \phi_c \}
$$
Here \(\mathbf{p}_s\) is the surface point, \(\mathbf{n}_s\) is the surface normal, \(\phi_r\) is the roughness estimate, and \(\phi_c\) is the compliance estimate. This terrain model is updated continuously as the humanoid robot moves forward.
The foot placement planner of the humanoid robot selects footholds that are both safe and efficient:
$$
\mathbf{p}_f^* = \arg\max_{\mathbf{p}_f \in \mathcal{F}} \left[ w_1 f(\mathbf{p}_f) + w_2 \Delta s(\mathbf{p}_f) – w_3 r(\mathbf{p}_f) \right]
$$
where \(\mathcal{F}\) is the set of feasible footholds, \(f(\mathbf{p}_f)\) measures contact quality, \(\Delta s(\mathbf{p}_f)\) measures forward progress, and \(r(\mathbf{p}_f)\) measures risk. In this way, the humanoid robot selects stable footholds without slowing down too much.
Balance is maintained using the zero-moment point criterion. The humanoid robot keeps its center of mass within a safe region relative to the support polygon:
$$
d_{zmp} = \min_{\mathbf{p} \in \mathcal{P}_{support}} \| \mathbf{p}_{zmp} – \mathbf{p} \|_2
$$
where \(\mathbf{p}_{zmp}\) is the zero-moment point and \(\mathcal{P}_{support}\) is the convex hull of the foot contact region. A larger margin \(d_{zmp}\) means that the humanoid robot is less likely to fall. During perception-based walking, this margin is actively maintained even when the terrain is uneven.
Climbing complex stair terrain. Another major technical leap is the ability of the humanoid robot to climb continuous staircases and large height differences. We integrated perception into the motion algorithm, so the humanoid robot no longer needs to have a perfect pre-programmed model of the stairs. Instead, it senses the step edges, plans foot placements, and executes motions without collision.
In our tests, the humanoid robot successfully climbed multiple stairs in succession and high steps up to 35 cm. The step feasibility condition can be expressed as:
$$
h_{step} \le 0.35 \, \text{m}, \quad d_{overlap} \ge \delta_{min}, \quad \mathbf{p}_f \in \mathcal{F}(\hat{\mathcal{T}})
$$
where \(h_{step}\) is the step height, \(d_{overlap}\) is the overlap distance between the foot and the stair surface, and \(\hat{\mathcal{T}}\) is the estimated terrain model. The humanoid robot was able to avoid stepping on edges, avoid collisions with vertical risers, and avoid missed steps. This is a remarkable achievement for outdoor operation.
| Capability | Before Upgrade | After Upgrade |
|---|---|---|
| Terrain perception | Reactive foot placement on flat floors | Vision-based perception walking |
| Stair climbing | Single small step | Continuous multi-step stairs, 35 cm high step |
| Running speed | 6 km/h | 12 km/h |
| Running terrain | Flat ground | Sand, snow, hillside |
| Outdoor robustness | Limited | First humanoid robot to climb multiple outdoor steps continuously |
I was especially impressed by the outdoor tests. In the past, the humanoid robot struggled with small obstacles. Now, it can handle uneven real-world terrain with staircases, slopes, and soft ground. This opens the door to rescue and exploration applications where the humanoid robot must walk through destroyed buildings or wild environments.
High-speed running on complex terrain. The humanoid robot also improved its running speed from 6 km/h to 12 km/h. This may sound simple, but running is a much harder problem than walking because it involves flight phases, high impact forces, and dynamic balance. The speed of the humanoid robot can be calculated as:
$$
v = \frac{\Delta s}{\Delta t}, \quad v_{max} = 12 \, \text{km/h} \approx 3.33 \, \text{m/s}
$$
To understand the dynamic regime of the humanoid robot, we use the Froude number:
$$
Fr = \frac{v^2}{g l}
$$
where \(v\) is the forward speed, \(g\) is gravitational acceleration, and \(l\) is leg length. For a humanoid robot with a leg length around one meter running at 3.33 m/s, the Froude number is above one, indicating a truly dynamic running gait. Achieving this on snow, sand, and hillside requires the humanoid robot to continuously adapt its stiffness, step length, and upper-body posture.
The humanoid robot became the first humanoid robot in the world to run on snow. This is significant because snow is deformable, slippery, and visually uniform. A typical vision system cannot always detect reliable features. The humanoid robot must combine visual cues with proprioceptive feedback and prior terrain knowledge. The result is a much stronger synergy between perception and the locomotion algorithm.
Stability and endurance. A humanoid robot is useful only if it can operate for extended periods without falling. The upgraded humanoid robot has high body stability, high algorithm robustness, and long endurance. We measure reliability using the mean time between falls, or the average duration of continuous task execution:
$$
\overline{T} = \frac{1}{M} \sum_{i=1}^{M} T_i
$$
where \(T_i\) is the operating time before the \(i\)-th failure and \(M\) is the number of failure intervals. In our tests, the humanoid robot maintained stable walking, running, and stair climbing over long test sessions. The combination of mechanical robustness, control robustness, and energy storage is essential for practical deployment.
From one humanoid robot to many. While the walking upgrade was happening, another revolution was taking place in industrial environments. I observed the first multi-robot, multi-scenario, multi-task collaborative training of humanoid robots in a 5G smart factory. Dozens of humanoid robot units were deployed across several areas, including the final assembly shop, the SPS instrument area, the quality inspection area, and the door assembly area.
This was not a simple demonstration. The humanoid robot units had to work together in real industrial tasks, including collaborative sorting, collaborative handling, and precise assembly. They had to avoid each other, coordinate timing, share information, and adapt to unexpected changes. This is a huge step from single-robot autonomous operation toward group intelligence.
For example, in the quality inspection area, the humanoid robot used its visual system to inspect parts, compare them with digital models, and communicate the results to other robots. In the final assembly area, multiple humanoid robot units worked side by side to carry heavy components and align them precisely. The image below captures the kind of quality inspection work that the humanoid robot now performs in the factory.
Group brain network. To support group intelligence, we introduced a software architecture called the humanoid robot group brain network, or BrainNet. This architecture uses cloud-edge collaborative reasoning nodes and skill nodes. The reasoning nodes form a “super brain” for the group, while the skill nodes form an “intelligent cerebellum” for individual robots. We also proposed an Internet of Humanoids concept, which connects humanoid robot units to the cloud, edge servers, and each other.
I like to model the BrainNet architecture as a graph:
$$
G_{BrainNet} = (\mathcal{V}_R, \mathcal{V}_S, \mathcal{E})
$$
where \(\mathcal{V}_R\) is the set of reasoning nodes, \(\mathcal{V}_S\) is the set of skill nodes, and \(\mathcal{E}\) is the set of communication links. In this graph, the humanoid robot is not an isolated agent; it is part of a distributed intelligence system.
The super brain uses a multi-modal embodied reasoning model. It receives observations from all humanoid robot units, resolves conflicts, decomposes complex production-line tasks, and makes high-dimensional decisions. The intelligent cerebellum uses Transformer-based models for cross-scene fused perception and multi-robot coordinated control. It also supports parallel distributed learning, allowing the humanoid robot group to acquire new skills quickly and transfer them from one robot to another.
Group decision-making. In the factory trial, the group decision-making process can be represented by:
$$
d_t = \Gamma\left( \bigoplus_{i=1}^{N} h_i^t, c_t \right)
$$
where \(h_i^t\) is the latent state of the \(i\)-th humanoid robot at time \(t\), \(c_t\) is the global context, \(\bigoplus\) is an aggregation operator, and \(\Gamma\) is the super brain reasoning model. The output \(d_t\) is a group-level decision, such as which robot should pick a part, which robot should move to another station, or which robot should pause to avoid congestion.
After the group decision is made, each humanoid robot executes its own local action:
$$
u_i(t) = \pi_i\left( x_i(t), h_i(t) \right)
$$
where \(x_i(t)\) is the local state of the \(i\)-th humanoid robot and \(h_i(t)\) is its local skill state. This separation between group reasoning and local control allows the humanoid robot group to scale to large numbers without requiring complete centralization of every joint command.
Multi-robot coordination. One of the core challenges for the humanoid robot group is coordination. We formulated the coordination problem as a distributed optimization task:
$$
\min_{\{u_i\}} \sum_{i=1}^{N} \left( \| x_i – x_i^* \|_{Q_i}^2 + \| u_i \|_{R_i}^2 \right) + \sum_{i<j} $$="" -=""
Here \(x_i^*\) is the desired state for the \(i\)-th humanoid robot, \(Q_i\) and \(R_i\) are weighting matrices, and \(\lambda_{ij}\) is a cooperation weight between robots \(i\) and \(j\). The second sum encourages the humanoid robot units to coordinate their actions so that they do not waste time or collide with each other.
This objective is solved in real time using decentralized optimization and consensus algorithms. Each humanoid robot communicates with its neighbors, exchanges predicted trajectories, and adjusts its own motion until the group reaches an agreement. This is how dozens of humanoid robot units can perform collaborative manipulation in a crowded factory environment.
Task decomposition and assignment. Another key function of the super brain is task decomposition. A complex factory-level task must be broken into subtasks that can be assigned to individual humanoid robot units:
$$
T = \{ T_1, T_2, \dots, T_M \}, \quad T_k \mapsto \mathcal{R}_k
$$
where \(T_k\) is the \(k\)-th subtask and \(\mathcal{R}_k\) is the subset of robots responsible for it. The assignment problem is solved using combinatorial optimization:
$$
\max_{x_{ij}} \sum_{i,j} c_{ij} x_{ij}
$$
$$
\sum_{i} x_{ij} = 1, \quad \sum_{j} x_{ij} = 1, \quad x_{ij} \in \{0,1\}
$$
where \(x_{ij}=1\) means that the \(i\)-th task is assigned to the \(j\)-th humanoid robot, and \(c_{ij}\) is the expected reward or cost. By solving this assignment, the humanoid robot group can minimize idle time and maximize productivity.
Distributed learning. The humanoid robot group also learns in a distributed way. Instead of collecting all data on a central server, each humanoid robot updates its skill model locally and then exchanges parameters with neighboring robots:
$$
\theta_i^{t+1} = \theta_i^t – \alpha \nabla \mathcal{L}_i(\theta_i^t) + \beta \sum_{j \in \mathcal{N}_i} \left( \theta_j^t – \theta_i^t \right)
$$
where \(\alpha\) is the learning rate, \(\beta\) is the consensus coefficient, and \(\mathcal{N}_i\) is the set of neighboring robots. This supports multi-robot parallel distributed learning, accelerates skill generation, and permits skill transfer from one humanoid robot to another. In the factory, this means that when one humanoid robot learns a more efficient assembly motion, the others can quickly benefit from that knowledge.
Joint policy and group intelligence. At the highest level, the joint behavior of the humanoid robot group can be represented by a joint stochastic policy:
$$
P(a_t^1, a_t^2, \dots, a_t^N \mid o_t^1, o_t^2, \dots, o_t^N) = \prod_{i=1}^{N} \mu_i(a_t^i \mid s_t^i, d_t)
$$
where \(o_t^i\), \(s_t^i\), and \(a_t^i\) are the observation, state, and action of the \(i\)-th humanoid robot at time \(t\), and \(d_t\) is the shared group decision. This formulation allows the humanoid robot group to act as a coherent team while preserving individual robot autonomy.
| Component | Function | Relevance to Humanoid Robot |
|---|---|---|
| Super Brain | High-level reasoning and planning | Decomposes factory-level tasks for the humanoid robot group |
| Intelligent Cerebellum | Low-level control and skill learning | Enables the humanoid robot to stabilize, walk, run, and manipulate |
| BrainNet | Software architecture for group intelligence | Links multiple humanoid robot units into a shared reasoning graph |
| Internet of Humanoids | Communication and connectivity layer | Connects humanoid robot units, edge nodes, and cloud services |
| Skill Nodes | Task-specific policies | Provide sorting, handling, and assembly skills to humanoid robot units |
Single robot versus group robot. The transition from a single humanoid robot to a group of humanoid robots is not just a matter of adding more machines. It changes the nature of decision-making, learning, and reliability. In my view, the humanoid robot will be most powerful when it can combine individual dexterity with collective intelligence.
| Dimension | Single Humanoid Robot | Group of Humanoid Robots |
|---|---|---|
| Task scope | Single workstation or narrow task | Production line or multi-station workflow |
| Decision-making | Local policy only | Global group decision plus local execution |
| Data availability | Individual experience only | Shared experience across all robots |
| Scalability | Limited by a single robot body | High scalability through BrainNet |
| Reliability | Single-point failure risk | Group redundancy and consensus |
| Communication | None | BrainNet and Internet of Humanoids |
I have observed this shift directly. During the first stage of industrial training, each humanoid robot worked alone at a specific workstation. It learned to pick parts, place components, or inspect quality by itself. In the second stage, the same humanoid robot units were connected through BrainNet. They began to communicate, coordinate, and make collective decisions. The improvement in efficiency was clear.
Industrial deployment scenarios. The factory trial included multiple complex scenarios. Each scenario demanded different skills from the humanoid robot group.
| Scenario | Task Type | Group Behavior |
|---|---|---|
| Final assembly area | Precise assembly | Multiple humanoid robot units coordinate to align and fix parts |
| SPS instrument area | Collaborative sorting | Humanoid robot units sort parts and deliver them to the right stations |
| Quality inspection area | Vision-based inspection | Humanoid robot units detect defects and share inspection data |
| Door assembly area | Co-manipulation | Several humanoid robot units carry and install doors together |
The most impressive result was that the humanoid robot group could handle a complete production line rather than a single isolated task. This is exactly what the manufacturing industry needs. In automotive, 3C manufacturing, and logistics, there are many tasks that require both mobility and dexterity. A wheeled robot cannot climb stairs or enter a human-designed workspace. A single humanoid robot cannot carry a heavy door alone or complete an entire assembly line by itself. A group of humanoid robot units, connected through a shared brain, can do all of these things.
The role of deep reasoning. A crucial part of the group intelligence architecture is the multi-modal reasoning model. This model gives the humanoid robot something similar to common-sense reasoning. It can understand high-level instructions, decompose them into concrete subtasks, and decide which humanoid robot should take each subtask.
We built the super brain around a deep-reasoning large language model. This model is not just a chat model; it is embodied and multi-modal. It receives text instructions, visual observations, and sensor measurements. It then outputs task plans and coordination commands. The deep-reasoning capability allows the humanoid robot to handle ambiguous situations. For example, if an assembly part is missing, the humanoid robot can reason about alternative actions instead of stopping and waiting for human help.
This is a major advance for the humanoid robot industry. In the past, robots could only execute predefined commands. Now, the humanoid robot can infer intent, predict consequences, and adapt its plan. The high-dimensional decision-making of the super brain can be represented as a mapping:
$$
d_t = \Gamma\left( \bigoplus_{i=1}^{N} h_i^t, c_t \right)
$$
where \(\Gamma\) is the deep reasoning model. Because this model is trainable, the humanoid robot group can continuously improve its reasoning ability as it encounters new tasks.
Energy efficiency and cost of transport. For any humanoid robot, energy efficiency is critical. Running and climbing are energy-intensive activities. We evaluate the energy performance of the humanoid robot using the cost of transport:
$$
CoT = \frac{P}{m g v}
$$
where \(P\) is the average power consumption, \(m\) is the total mass, \(g\) is gravitational acceleration, and \(v\) is the forward speed. A lower cost of transport means that the humanoid robot can operate longer with the same battery. The upgraded humanoid robot improved its energy efficiency through better gait patterns and more compliant actuation.
Long endurance is not only about battery capacity. It is also about mechanical efficiency, algorithm efficiency, and thermal management. During our factory trials, the humanoid robot worked for long periods in the quality inspection and assembly areas. The group coordination architecture reduced unnecessary movements, saving energy and time.
Reliability and safety. When a humanoid robot works near human workers, safety is essential. We implemented force limits, collision avoidance, and emergency stop mechanisms. The humanoid robot can detect unexpected contacts and react quickly. In group scenarios, the humanoid robot units also monitor each other. If one robot stops unexpectedly, the others can reroute their tasks.
This safety layer is based on real-time control. The humanoid robot continuously checks the distance between its body and nearby objects or people:
$$
d_{safe} = \min_{i,j} \| \mathbf{p}_i – \mathbf{p}_j \|_2
$$
where \(\mathbf{p}_i\) and \(\mathbf{p}_j\) are points on different objects or robots. If \(d_{safe}\) falls below a threshold, the humanoid robot reduces its speed or changes its path. This is especially important in a factory where multiple humanoid robot units move in the same workspace.
Simulation and real-world transfer. Another important part of our humanoid robot development is simulation-based training. We train many skills in a simulated environment, then transfer them to the real humanoid robot. The reality gap is bridged through domain randomization and system identification. The mathematical formulation for sim-to-real transfer involves optimizing the simulation parameters to match real sensor and actuator data:
$$
\min_{\phi_{sim}} \mathbb{E}_{d_{real}}\left[ \| \tau_{real} – \tau_{sim}(\phi_{sim}) \|^2 \right]
$$
where \(\phi_{sim}\) are simulation parameters, \(\tau_{real}\) is the real torque or force measurement, and \(\tau_{sim}\) is the simulated counterpart. The result is that the humanoid robot can learn robust behaviors in simulation and deploy them safely in the real world.
Challenges and future directions. Despite these achievements, I believe the humanoid robot still has a long way to go. The first challenge is battery life. High-speed running and heavy manipulation consume a large amount of energy. The humanoid robot needs higher-density batteries, lighter materials, and more efficient actuators.
The second challenge is dexterity. Current humanoid robot hands are far less capable than human hands. We need better tactile sensors, stronger and softer fingers, and more precise control algorithms. The humanoid robot should be able to handle cables, small screws, and fragile objects.
The third challenge is general-purpose learning. The humanoid robot needs to learn new skills quickly, without seeing thousands of examples. We are exploring meta-learning and foundation models for robots. The goal is to create a humanoid robot that can learn a new task from one or two demonstrations.
The fourth challenge is group coordination in unstructured environments. In a factory, we can place markers, define precise layouts, and use reliable Wi-Fi. In a disaster zone, the humanoid robot group may have no communication infrastructure. The humanoid robot must use decentralized protocols and peer-to-peer communication to stay connected.
I am also thinking about human-robot interaction. A humanoid robot should understand gestures, speech, and social cues. It should know when to ask for help and when to work autonomously. This is not only a technical problem but also a user experience problem. The humanoid robot must be predictable and trustworthy.
A table of key evaluation metrics. To summarize the performance of the humanoid robot, I often use the following evaluation metrics.
| Metric | Description | Goal |
|---|---|---|
| Walking speed | Average forward speed during walking | Higher speed without losing stability |
| Running speed | Maximum forward speed with flight phase | 12 km/h or more |
| Step height limit | Maximum vertical obstacle step | 35 cm or more |
| ZMP margin | Distance from ZMP to support polygon boundary | Maximize margin |
| Task success rate | Percentage of successful task executions | Above 99 percent in structured environments |
| Mean time between failures | Average operation time before a fall or stop | Increase endurance |
| Communication latency | Delay between humanoid robot units and super brain | Minimize latency |
Why the humanoid robot matters. I often ask myself why we are investing so much effort in the humanoid robot rather than simpler robot forms. The answer is that the humanoid robot is uniquely suited to human environments. Our stairs, doors, tools, and vehicles are designed for human bodies. The humanoid robot can use the same infrastructure without requiring major modifications.
The humanoid robot can also work in dangerous environments. In rescue operations, it can enter collapsed buildings, climb debris, and search for survivors. In exploration, it can walk on planetary surfaces or in hazardous industrial sites. In health care, it can help nurses lift patients or deliver supplies. In manufacturing, it can perform tasks that are too dangerous or too repetitive for human workers.
The latest technical upgrade strengthens each of these possibilities. With perception-based walking, the humanoid robot can navigate unpredictable terrain. With complex stair climbing, it can enter multi-level buildings. With high-speed running, it can respond quickly to emergencies. With group intelligence, a fleet of humanoid robot units can cooperate to accomplish large-scale tasks.
First-person observations. During the factory trial, I watched a group of humanoid robot units coordinate their movements in the door assembly area. One humanoid robot held the door while another robot inserted the hinges. A third robot brought the screws and tools. The group performed this task more smoothly than many humans would. The key was not just individual skill, but the shared group brain. Every humanoid robot knew the state of the task and the intentions of the other robots.
I was also present when the humanoid robot ran across a snow-covered test field. The surface was slippery and uneven. The humanoid robot adjusted its posture and step length in real time. It did not fall. For me, that moment symbolized the arrival of a new generation of robotics. The humanoid robot is no longer confined to a laboratory mat.
Impact on industry dynamics. The industry dynamics are changing rapidly because of these breakthroughs. Companies are moving from single-robot automation to group robot deployment. The humanoid robot is becoming a platform, not just a machine. With the BrainNet architecture, multiple humanoid robot units can be trained as a team and deployed as a team. This reduces deployment time and increases flexibility.
In the first stage of industrial training, the humanoid robot worked alongside automotive, 3C manufacturing, and logistics partners. Each partner contributed valuable task data and operational knowledge. In the second stage, the humanoid robot entered a 5G smart factory and performed real multi-task collaborative work. This is an important milestone because it proves that the humanoid robot can survive the transition from laboratory to factory floor.
Continuous improvement. The humanoid robot will continue to improve. I expect to see faster walking, longer endurance, better hands, and more advanced reasoning. I also expect the group intelligence architecture to evolve. The Internet of Humanoids could one day connect thousands of humanoid robot units across multiple factories and cities.
Imagine a scenario where a humanoid robot in a factory learns a new skill, and immediately every other humanoid robot in the network can use that skill. That is the promise of BrainNet and distributed learning. The humanoid robot group becomes a learning organism. Each robot contributes to the collective knowledge.
Mathematical summary. The core mathematical tools behind the humanoid robot include rigid-body dynamics, convex optimization, deep reinforcement learning, graph neural networks, and distributed control. These tools work together. The humanoid robot uses optimization for control, neural networks for perception, and group algorithms for cooperation. I believe this combination is the right formula for general-purpose embodied intelligence.
The dynamic model of the humanoid robot determines what motions are physically possible. The perception model determines what the humanoid robot knows about the world. The policy model determines what actions the humanoid robot takes. The group model determines how multiple humanoid robot units interact. All of these models must be aligned with each other.
A vision for the future. In the future, I see the humanoid robot becoming as common as the smartphone or the car. It will be used in homes, hospitals, factories, farms, and disaster zones. It will assist elderly people, teach children, build houses, and explore distant planets. The humanoid robot will not replace humans; it will amplify human capability.
But there are still many technical and ethical challenges. We must design the humanoid robot to be safe, private, and controllable. We must ensure that the humanoid robot is used for good purposes. We must also make humanoid robot technology affordable and accessible. The latest upgrade is a step in that direction, but it is only the beginning.
Conclusion. I have seen the humanoid robot go through an extraordinary transformation. It can now walk with visual perception, climb high steps, run on snow, and collaborate with dozens of other humanoid robot units in a smart factory. These breakthroughs are not isolated. They all come from a deeper trend: the convergence of embodied intelligence, deep reasoning, and distributed group learning.
The humanoid robot is becoming a new kind of industrial worker, rescue agent, and daily assistant. I am excited to be part of this journey. The humanoid robot still has many limitations, but the progress is undeniable. With each new upgrade, the humanoid robot moves closer to the goal of universal physical assistance. In the coming years, I believe the humanoid robot will leave the laboratory and enter nearly every corner of human life.
I hope this article gives you a clear picture of the state of humanoid robot technology from my perspective. The road ahead is long, but the direction is clear. The humanoid robot has crossed the threshold from demonstration to deployment. From now on, every step on the factory floor or in the snow is a step toward a future where intelligent machines work together with us to build a better world.