How the Evolution of Large Language Models Will Reshape Embodied Intelligence

In recent years, general large language models (LLMs) have evolved significantly faster than traditional embodied intelligence systems. As models continue to improve in visual understanding, long-term reasoning, code generation, video analysis, and multimodal interaction, a critical question arises: Will the continued evolution of general LLMs directly break through the current technical bottlenecks of embodied intelligence and alter the competitive landscape of the entire industry?

This is not simply a matter of “larger model parameters automatically making robots smarter.” Embodied intelligence faces an open, continuous, and uncertain physical world. Models must not only understand instructions but also perceive the environment, plan actions, control the body, handle failures, and complete tasks within safety boundaries. Therefore, the impact of general LLMs on embodied intelligence is more likely to manifest as a reconstruction of system architecture, R&D methods, and industry division of labor.

1. Why General Capabilities Might Transfer to Robotics

Traditional robotic systems typically use modular designs: a vision module identifies objects, a planning module generates paths, a control module executes actions, and a state machine handles exceptions. While these systems offer good stability in structured environments, they often require extensive manual rule-coding when object positions change, task descriptions are incomplete, occlusions occur, or execution deviates.

General LLMs offer an alternative approach. They can process natural language, images, video, spatial relationships, and action outcomes within the same reasoning framework. For example, given a task like “put the items on the table into the heating device,” the model must not only know what the target objects are but also understand the container’s location, whether the device is open, whether the tray is in place, and whether the current action is producing the expected result.

The key to this capability is not that the model “memorizes a specific action,” but rather its ability to form the following general skills:

1. Semantic understanding: Understanding task goals, constraints, and implicit intentions.
2. Spatial reasoning: Determining distance, direction, occlusion, and reachability between objects.
3. Step planning: Breaking down complex tasks into executable sub-tasks.
4. Result verification: Judging whether an action was successful rather than assuming it always works.
5. Error recovery: Re-planning upon failure instead of following an erroneous path.

If these capabilities can be stably transferred to robotic control, embodied intelligence systems will gradually shift from “executing predefined flows” to “understanding goals and completing tasks autonomously.”

2. The Most Direct Change: From Flow Execution to Closed-Loop Decision-Making

The most critical technical feature of embodied intelligence is the closed loop. A closed loop means the system continuously undergoes an “observe-decide-execute-observe” process rather than generating a complete sequence of actions all at once.

For instance, during a dual-arm assembly task, the model might first generate the following steps:

• Identify target components and installation locations.
• Adjust robotic arm postures.
• Grasp the components.
• Align with the interface.
• Apply appropriate force.
• Check if the installation is secure.

If the interface is misaligned or the component shifts during grasping, the system must re-evaluate based on new visual and tactile information. Models with general reasoning capabilities can use “action results” as input for the next round of decision-making, forming a more flexible feedback loop.

This is distinctly different from traditional script-based control. Scripts typically assume that the environment meets expected conditions; if not, they enter exception branches or fail directly. LLMs, however, can attempt to explain the cause of failure, such as:

• The target object is occluded; the observation angle needs adjustment.
• The tray is not fully pushed in; a pushing action must be added.
• The grasp position is unstable; a new contact point must be selected.
• The part failed to insert; the direction must be changed or speed reduced.
• The current action poses a collision risk; the path must be re-planned.

Of course, this does not mean every action generated by an LLM is reliable. Its value lies primarily in improving exception handling and task re-planning. The final actions should still be constrained by low-level controllers, collision detectors, and safety policies.

3. World Models as the Connection Between LLMs and Embodied Intelligence

One of the long-standing core issues in embodied intelligence is the lack of stable predictive capabilities for the physical world. Robots cannot merely know “what an object is”; they must also predict “what will happen if a specific action is applied to the object.”

For example:

• Which direction will a box move when pushed?
• Will a flexible object deform or slip when grasped?
• How much force is needed to rotate a valve?
• Will a collision occur when a robotic arm reaches into a narrow space?
• Where might an object be located if it is occluded?

These questions generally fall under the scope of world models or environment models. By processing vast amounts of visual, language, video, and interaction data, general LLMs can acquire a certain degree of common-sense reasoning and predict object relationships and event changes. This capability may reduce embodied systems’ reliance on manual rules and specialized data.

However, it must be noted that “reasonable inference” at the language and visual levels does not equate to precise physical simulation. A model might know that glass is fragile but fail to accurately calculate the gripping force; it might know that two objects occlude each other but cannot directly derive an executable 3D pose. Therefore, a more realistic future approach is not to let general LLMs completely replace physical models, but to have them collaborate:

• The general model handles task understanding and high-level reasoning.
• The vision model handles localization, segmentation, and pose estimation.
• The physical model constrains motion and contact relationships.
• The low-level controller handles high-frequency, stable, real-time action execution.
• The safety module prevents dangerous behaviors and boundary violations.

4. On-Site Learning as a Key Advantage of General LLMs

Traditional embodied models often require training for specific robots, environments, and tasks. Changing a robotic arm, altering the workbench layout, or even swapping an object’s material can lead to significant performance drops.

The advantage of general LLMs lies in their on-site adaptability. Through a few demonstrations, text descriptions, or continuous observation, they can quickly understand task rules. For example, an operator only needs to tell the system, “First place the container in the positioning area, then install the fitting, and check for looseness after installation.” The model can then generate a preliminary execution plan based on the on-site environment.

On-site learning manifests primarily at three levels:

1. Task-Level Adaptation

The model can understand new task goals without needing a complete program redesign for each task.

2. Environment-Level Adaptation

When the workbench, object positions, or operation sequences change, the model can adjust its plan based on current observations.

3. Failure-Level Adaptation

The model can transform failure cases into decision-making references, avoiding the repetition of proven ineffective actions.

However, on-site learning does not mean the system can engage in unlimited trial-and-error. In real-world environments, trial-and-error costs may include equipment damage, personal injury, or production accidents. Therefore, on-site learning should be conducted in a tiered manner across simulated environments, digital twin environments, and controlled physical environments, with clear action permissions set.

5. Practical Impact on the Embodied AI Industry

If general LLMs continue to strengthen, the industry landscape may experience several noticeable changes.

1. The Rise of Foundation Models

In the past, embodied intelligence companies often had to build their own perception, language understanding, task planning, and data closed-loop systems. In the future, some general capabilities may be provided by foundation models, shifting the competitive focus toward the robot body, scenario data, action data, safety systems, and engineering delivery capabilities.

2. Lower Technical Barriers for SMEs

General models can reduce the cost of training language understanding and high-level planning modules from scratch, making it easier for small teams to build prototype systems. A team with robotics hardware and scenario experience can develop applications for warehousing, inspection, home assistance, or lab operations without independently training a complete LLM.

3. Weakening of Pure Data Scale Advantages

If foundation models can achieve transfer through few-shot demonstrations, the value of merely accumulating large amounts of static data will decline. Truly valuable data will be high-quality interaction data, including action trajectories, failure records, contact states, environmental changes, and safety incidents.

4. System Integration Capabilities Become Crucial

Embodied intelligence is not a single-model product but a complex system composed of models, sensors, actuators, control systems, task management systems, and safety mechanisms. In the future, a company’s core competency may no longer be “whether it has a larger model,” but whether it can stably deploy the model in a real production environment.

6. Safety Alignment: The Biggest Obstacle for General Models in the Physical World

In text or digital environments, model output errors can usually be undone or regenerated. In the physical world, however, incorrect actions can lead to irreversible consequences. For example, a robot misidentifying a high-temperature device, incorrectly grasping a sharp object, or continuing to move when someone approaches can cause severe accidents.

Therefore, safety issues in embodied intelligence encompass at least the following levels:

1. Intent safety: Does the model understand what the user actually wants to accomplish?
2. Action safety: Will the generated action cause collision, crushing, falling, or damage?
3. Environment safety: Are there risk factors around, such as personnel, flammables, or high-voltage equipment?
4. Permission safety: Is the robot authorized to perform the current operation?
5. Exception safety: Can the system stop in time when sensors fail, communication is interrupted, or the model is uncertain?

Relying solely on text-level refusals cannot solve the problem. A more reliable approach should adopt multi-layer protection:

• Conduct task risk assessment before model output.
• Perform collision and reachability checks on action trajectories.
• Set hard constraints on speed, force, and work areas.
• Require human confirmation for high-risk actions.
• Enter a safe stop when model confidence is insufficient.
• Log complete perception, decision, and execution data for traceability.

Furthermore, safety testing should not only verify whether the model “rejects dangerous instructions” but also test whether it adopts safe behaviors when instructions are ambiguous, the environment is abnormal, sensors fail, or multi-task conflicts occur.

7. Why General LLMs Cannot Fully Replace Embodied Models in the Short Term

Although general models have great potential, “directly covering embodied intelligence” still faces several practical limitations.

First is real-time performance. Robotic control often requires millisecond-level or higher frequency feedback, while large model inference typically suffers from latency, making it difficult to directly handle all low-level control tasks.

Second is precision. Natural language and visual reasoning can tolerate a certain degree of ambiguity, but mechanical assembly, precision grasping, and complex contact tasks are highly sensitive to position, force, and timing errors.

Third is the data distribution problem. Internet data can help models understand world common sense, but it cannot completely replace real robot interaction data. Physical contact, friction, flexible deformation, and mechanical errors still need to be obtained through real-world collection or high-quality simulation.

Finally, there is the boundary of accountability. Enterprise production environments require systems to be explainable, verifiable, rollback-capable, and auditable, whereas the internal decision-making process of general models does not naturally meet these requirements.

Therefore, the future is more likely to see a collaborative architecture where “general LLMs act as the brain, and specialized models act as the cerebellum.” The former is responsible for understanding and planning, while the latter handles real-time control and safe execution.

8. Future Development Path

From a technological evolution perspective, embodied intelligence may develop along the following route:

Phase 1: Model-assisted robots, using natural language for task description and high-level planning.
Phase 2: Multimodal closed-loop systems, where models continuously read visual, tactile, and action feedback, and correct failures.
Phase 3: Agents with environmental prediction capabilities, capable of comparing the risks and benefits of different plans before execution.
Phase 4: Transferable general robot systems, where the same set of models can adapt to different hardware, scenarios, and tasks.
Phase 5: Robot swarms with safe autonomy, capable of collaborating on complex tasks under clear permissions and constraints.

This process will not be determined solely by model scale; it also depends on the joint progress of hardware costs, data quality, simulation capabilities, control algorithms, and safety standards.

Overall, general LLMs do indeed have the potential to become a key force driving the leapfrog development of embodied intelligence. They can enhance robots’ task understanding, on-site learning, exception handling, and cross-scenario transfer capabilities, and may also change the R&D methods and competitive thresholds of embodied intelligence companies.

However, a more accurate judgment is: General LLMs will reshape embodied intelligence rather than simply replace it. A truly mature system must combine general reasoning, physical modeling, real-time control, and safety constraints. Whoever can率先 establish a stable data closed-loop, reliable action verification mechanism, and auditable safety system will be more likely to translate model capabilities into real-world productivity.