While it remains uncertain whether OpenClaw has disrupted traditional work patterns, it's evident that embodied AI is on the cusp of a significant transformation.

Recently, a surge of online videos has showcased netizens, unsatisfied with mere 'cyber shrimp farming' (a term often used to describe trivial or low-effort online activities), connecting OpenClaw to cameras and robotic arms. This integration has revealed that OpenClaw is not only proficient in computer-based tasks but also highly capable in real-world scenarios.

For instance, some users have equipped OpenClaw with a computer, robotic arm, and camera. Without the need to rewrite programs or train separate models for specific tasks, they simply instructed OpenClaw: 'Sort these car parts.'

OpenClaw then efficiently completed the sorting task.

What implications does this hold for embodied AI?

Consider this: less than a year ago, achieving such capabilities would have necessitated a dedicated press conference and millions of dollars in global promotion by humanoid robot companies.

Yet now, OpenClaw has effortlessly attained these same capabilities, despite not being specifically designed as an embodied AI tool.

This development seems somewhat surreal.

So, what exactly has OpenClaw contributed to the field of humanoid robots? Given its formidable strength, do specialized embodied large models still hold relevance? Have the efforts of robotics companies been rendered obsolete? Why can OpenClaw easily accomplish what has eluded robotics companies for years?

And when the dust settles, who will be left exposed?

When 'Shrimp Farming' Extends to Robots

I still recall that around early April last year (2025), a leading domestic humanoid robot company held a solemn press conference in Beijing to unveil its humanoid robot development platform.

At the time, the platform's standout feature was its ability to sort scattered industrial parts through voice commands alone, with smooth movements and minimal errors.

Does this sound familiar? It's almost identical to what OpenClaw can achieve today.

The key difference lies in the fact that the platform released by this company was specifically tailored for robots. It dissected numerous scenarios, trained agents, and connected them through behavioral path planning, involving a substantial amount of work.

At the time, the company's promotional slogan for the platform was: 'The most crucial piece for humanoid robots to transition from performance to practical work and from the laboratory to the factory.' Now, OpenClaw seems to have effortlessly achieved similar capabilities, yet it clearly hasn't undergone such a process.

This is akin to climbing a mountain with a friend. You meticulously prepare, depart early, and spend a significant amount of time, finally arriving at the summit breathlessly, only to find that your friend has been waiting there for you, having arrived by helicopter.

Specifically, OpenClaw has demonstrated strong generalization, decision-making, and self-evolution capabilities across various scenarios.

For example, in one experiment, there was a more lifestyle-oriented test. Staff instructed the robotic arm: 'It's the Lantern Festival today; make me some sweet rice dumplings.'

The robotic arm paused to consider the task before executing it: pouring soup into a pot, adding the dumplings, and waiting for the water to boil.

Midway through, the staff asked, 'Can you add some sugar?'

The robotic arm responded, 'Brown sugar or osmanthus sugar?'

After receiving the answer 'brown sugar,' it poured the sugar into the pot.

Additionally, various experiments have been conducted. For instance, developers have connected OpenClaw to industrial robotic arms, enabling them to complete tasks such as grasping, carrying, and transporting based on natural language instructions. The system even automatically generates Python scripts to control the robotic arms.

Besides robotic arms, quadrupedal robots have also quickly appeared in various 'shrimp farming' experiments.

In some videos circulating on Reddit and X, developers have connected OpenClaw to robotic dogs, enabling them to patrol autonomously in their environment.

In the past, such robots typically required remote control or followed pre-designed routes. However, in these experiments, without manipulation or pre-planned routes, the robotic dogs judged and planned their paths based on the environment perceived by their cameras, such as avoiding obstacles or replanning their routes when encountering new situations.

When these experiments began to involve humanoid robots, things became even more intriguing.

For example, in an open-source community, someone released a set of Unitree-robot skills compatible with OpenClaw. With this integration, developers can directly control Unitree robots, such as the G1, or even larger models like the H1, as well as quadrupedal robots GO1 and GO2, through instant messaging software.

The entire process is much simpler than imagined. Developers do not need to open complex graphical interfaces or manually invoke SDKs. They can simply send a message in the chat window:

'Move forward one meter.'

'Turn left 45 degrees.'

The robot will then execute the corresponding actions.

This control is even bidirectional. OpenClaw can acquire environmental images from the stereo cameras mounted on the robot and send the screenshots directly back to the chat window, allowing developers to view the scene in real-time at any time. If a path planning module is also integrated, the system can automatically plan routes and avoid obstacles.

Again, the entire process involves no pre-set scripts or pre-planned action paths.

Developers merely provide a goal, and the AI handles the rest, making its own judgments and plans.

Can a Crayfish Overturn Humanoid Robots?

From various demonstration videos, we have witnessed the astonishing capabilities demonstrated by OpenClaw in combination with other large models.

In the past, these would have been the proudest achievements of many humanoid robot companies, but now they have become commonplace.

This inevitably raises a question: Are the capabilities that the robotics industry has spent years acquiring data, training models, and developing systems to achieve still valuable?

The answer is, of course, no.

To understand this, let's start from the basics. A robot, besides its physical body, has a decision-making system that can be roughly divided into four layers from top to bottom:

Decision Layer (Brain): Understands goals and task decomposition; Perception/Representation Layer: Identifies the environment, targets, and spatial states; Behavior Organization Layer: Breaks tasks down into skill and action sequences; Control Layer (Cerebellum): Handles trajectory, servo control, obstacle avoidance, and safe execution.

Within this framework, OpenClaw primarily handles the invocation, orchestration, and connection of the capabilities in the preceding layers. As for how the robot ultimately moves and whether its actions can be stably executed, these still depend on the underlying control system, kinematic solutions, and execution pathways.

Therefore, OpenClaw does not suddenly teach robots how to move; instead, it functions more like an upper-level scheduling system that translates human instructions into a series of invokable capabilities.

There are two truly noteworthy highlights here.

First, OpenClaw has changed how robots acquire these capabilities.

In the past, achieving many capabilities was not impossible, but it often required significant data collection, specialized training, and complex rule engineering for single tasks.

Now, OpenClaw can directly leverage mature multimodal models, tool systems, and modular execution pathways to transform many capabilities that previously required separate development and training into directly invokable and rapidly combinable capabilities.

As a result, for the same grasping, searching, or inspection task, development efficiency is higher, trial-and-error cycles are shorter, and overall costs are lower.

Second, OpenClaw has enabled robots to begin acquiring a capability that was rarely truly established before: continuous memory of the real world.

Traditional robots primarily operate in the 'present.' They react to what they see; once the task is completed, their understanding of the environment largely remains at that moment. Many systems can, of course, create maps, perform localization, and save task states, but they typically do not continuously organize 'locations, objects, events, and times' into a unified memory structure that can be invoked at any time.

Now, OpenClaw is attempting to organize the important objects, locations, events, and times perceived by robots into a retrievable spatiotemporal semantic memory.

This means that robots are no longer just executing commands; they are continuously accumulating context.

When someone enters a room, where an object is placed, or when a behavior occurs, these may all be incorporated into the basis for subsequent searches, judgments, and actions.

Of course, this does not mean it has acquired a complete understanding of the world like humans, but at least it indicates that it is beginning to develop a structured memory capability oriented towards the real world.

The significance of this lies in the fact that the boundaries of robot capabilities are extending from 'completing a single task' to 'continuously understanding an environment.' (In the same or similar environments, continuous context enhances task continuity and local stability, but this does not equate to the system having acquired broad generalization capabilities.)

Of course, OpenClaw's ability to achieve these feats does not arise out of thin air; there are two important underlying reasons.

The first reason is the recent changes in the underlying architectures of robots themselves.

In the past, many robotic systems resembled closed silos: perception was one system, planning was another, and control was yet another, with complex interconnections and high development thresholds. Many capabilities already existed but were difficult to invoke flexibly.

Now, robotic systems are becoming increasingly modular and standardized. Cameras, robotic arms, grasping modules, path planning, and low-level control interfaces are gradually becoming pluggable and combinable capability units.

The reason OpenClaw appears so powerful is not that it has created underlying robotic capabilities out of thin air, but rather that it can stand on top of a gradually standardizing execution stack and reorganize these capabilities.

The second reason is the rapid integration of dispersed capabilities by multimodal large models.

In the past, if a humanoid robot wanted to complete a task, it often had to solve many problems separately: text understanding, speech recognition, image recognition, video understanding, target detection, spatial judgment, and task decomposition often had to be handled by different modules.

Now, multimodal large models can simultaneously process different types of information, such as text, images, speech, and video, and unify their understanding within the same context. This means that the perception and understanding capabilities that robots previously had to train and integrate separately are being gradually absorbed by more general-purpose foundational models.

This has significantly lowered the development threshold for upper-level robot intelligence. And this is the significance of OpenClaw; it does not reinvent these capabilities but more efficiently integrates these enhanced general-purpose capabilities into robotic systems.

Do Embodied Large Models Still Hold Significance?

This naturally leads to a more crucial question: Given that foundational models are becoming increasingly powerful, is there still significance in developing specialized embodied intelligence large models?

After all, many humanoid robot companies had previously announced with great fanfare their self-developed embodied large models, viewing them as their most important strategic core, as if whoever mastered embodied models would master the future of robots.

But now, it seems that general-purpose foundational models are rapidly acquiring understanding, perception, and task orchestration capabilities, and many of the upper-level capabilities that robotics companies have spent years building are being quickly generalized by larger foundational model systems.

The answer is: Yes, and they remain crucial.

The reason is that while foundational models are becoming stronger, they primarily enhance a robot's ability to 'understand the world'; what embodied models truly determine is a robot's ability to 'execute actions in the physical world.'

Understanding a sentence, recognizing a target, and decomposing a task are indeed becoming more like general-purpose capabilities. However, the most challenging part for robots has never been just understanding or seeing; it is whether their actions are truly valid in the real world—whether the grasping angle is correct, whether the trajectory is stable, whether the contact force remains controlled, whether they can continue after the target is obscured, whether they can recover after a failed grasp, and whether they can still succeed in a different scenario, with a different object, or on a different machine.

These issues cannot be automatically resolved by stronger 'understanding capabilities' alone.

The value of embodied intelligence large models lies not in handling everything but in precipitating a large amount of experience related to actions, operations, and interactions, enabling robots to form stable, reusable, and generalizable capabilities beyond just creating a demo.

In other words, general-purpose models are consuming the 'understanding layer'; what embodied models defend is still the 'action layer' and the 'physical implementation layer.'

Therefore, embodied models are not without significance; their role is simply evolving: Previously, they resembled 'full-stack brains' attempting to handle everything; now, they are more like the critical layer within the entire robotic system that determines capability ceilings.

Finally, let's return to the original question: What exactly has OpenClaw brought to the humanoid robotics industry?

The answer lies in the fact that it has compelled the entire industry to acknowledge a reality much earlier than anticipated: The high-level task intelligence of humanoid robots is swiftly becoming a widespread phenomenon.

In the past, for numerous companies, the most elusive capability was integrating comprehension, perception, planning, and execution into a functional system. However, with the advancement of multimodal foundational models and agent frameworks, this barrier is quickly diminishing.

Developing a commendable demonstration will become progressively simpler, indicating that the robotics industry is venturing into more challenging territories.

Future competition will no longer revolve around who can first create a demonstration that 'understands instructions.' Instead, it will focus on who can stabilize actions, enhance success rates, and develop systems that are low-latency, replicable, mass-producible, and safely deployable. The true determinants of success will be more fundamental professional competencies: control, data, robustness, engineering, and mass production capabilities.

In essence, OpenClaw has lowered the barrier to creating demonstrations but has not diminished the complexity of developing products.

And this is precisely its most profound impact on the industry: Companies that remain superficial and depend on handcrafted demonstrations to weave narratives will swiftly witness their competitiveness diminish; only when the tide goes out will it be evident who has been swimming without clothes.