The Practical Impact of General-Purpose Large Models on Embodied AI

As general-purpose large models continue to improve, whether robots still need their own models is moving from a technical thought experiment to a question the industry must answer. Large models can already understand natural language, recognize complex scenes, decompose task steps, and to some extent generate executable action plans. These capabilities significantly lower the interaction barrier for robot systems and push embodied AI from fixed-flow automation toward autonomous execution in open environments.

It is important to be clear, however: the fact that a model can participate in solving a robot problem does not mean it is the model best suited to robots. Tasks in the digital world usually use text, images, code, or video as their primary information carriers. Robots face a real environment that changes continuously, contains noise, and imposes physical constraints. A robot must not only know what should be done; it must also judge within milliseconds or less whether an action can be performed now, how to perform it more stably, and what consequences a failed action may bring.

The impact of general-purpose large models on embodied AI should therefore not be judged only by whether a model can complete a particular demonstration task. It should be analyzed through how it changes robot system architecture, data production methods, training pipelines, interaction models, and safety mechanisms.

1. General-purpose large models first change how robots understand tasks

Traditional robot systems often rely on structured instructions. For example, a user must explicitly specify the target position, action sequence, and object to be manipulated, and the system then executes according to preset rules. This approach is stable, but it places high demands on users and has weak adaptability when the environment changes.

The introduction of general-purpose large models enables robots to handle more natural and ambiguous task descriptions. For example, “tidy the desk so it is suitable for a meeting” is not a single action that can be executed directly. It involves object recognition, spatial judgment, priority decision-making, and result evaluation. A large model can decompose such a high-level goal into several sub-tasks:

1. Determine which objects are on the desk;
2. Distinguish objects that should be kept, moved, or removed;
3. Estimate where each object should be placed;
4. Plan the grasping and moving sequence;
5. Dynamically adjust the plan based on execution results.

This means the input to a robot system is no longer limited to fixed commands. It can shift toward integrated understanding of natural language, visual scenes, historical task records, and environment states.

However, large models are better suited to high-level semantic understanding and task planning; they should not directly replace all low-level control modules. Phrases such as “put it down gently” or “try not to knock over nearby objects” need to be converted into computable conditions such as speed, force, trajectory, and collision constraints. This conversion still requires dedicated robot models and control algorithms.

2. From vision-language models to vision-language-action models, the key change is the action closed loop

Early multimodal models mainly addressed the problems of “seeing and understanding” and “describing clearly.” Robot applications require models to go further: what action should be taken next, and how will the environment change after the action is completed?

This has driven the development of vision-language-action (VLA) models. Such models attempt to place images, language, and robot actions in a unified framework so that the system can directly generate action sequences based on the scene and goal. Their value is mainly reflected in three areas.

2.1 Lower task programming costs

In the past, writing rules, designing state machines, and debugging action parameters for each new task often required substantial engineering work. Models with action-generation capabilities can complete initial task configuration through a small number of examples or natural language descriptions, shortening robot deployment cycles.

2.2 Improve cross-task transfer capability

If a model only memorizes a fixed trajectory for one task, it may fail when the environment changes slightly. A more desirable system should understand the goal behind the task. For example, the goal “place an object into a container” can apply to objects of different shapes, containers of different heights, and different workspaces.

2.3 Support continuous interaction and dynamic adjustment

Real environments rarely run exactly as expected. Objects may slide, target positions may be occluded, and grasping actions may fail. The model needs to continuously read new sensor information, judge the current state, and correct actions in time, rather than generating a complete plan once and executing it mechanically.

This shows that the core of embodied AI is not simply generating actions. It is establishing a closed loop of observation, decision, execution, feedback, and correction.

3. Why general-purpose large models cannot directly carry all robot intelligence

General-purpose large models are usually trained on large-scale digital data and are good at abstract concepts, language reasoning, and pattern induction. Robots, however, face a different type of problem.

3.1 The physical world is continuous

Text and images can be represented discretely, while robot actions usually change continuously. The pose, joint angles, speed, torque, and contact state of a robotic arm can all affect the final result. A very small control error can cause a grasp failure or collision.

3.2 Physical interaction is irreversible

If a digital system generates incorrect content, it can usually be recomputed. If a robot performs an incorrect action, it may damage objects, affect equipment operation, or even create personal risk. A robot therefore needs to judge feasibility before execution, monitor safety during execution, and verify results after execution.

3.3 Sensor information contains noise and latency

Cameras, force sensors, tactile sensors, and position sensors can all be affected by occlusion, lighting, vibration, material properties, and hardware errors. A model cannot rely only on a single frame of visual results. It needs to fuse multi-source information and understand the trend of changes in the environment state.

3.4 Real-time requirements differ

High-level task planning can tolerate some delay, but obstacle avoidance, contact control, and pose correction usually require fast responses. If all decisions are handed to a general-purpose model with a large parameter scale, there may be problems with response speed, computing resources, and stability.

A more realistic technical path is therefore not to have one model handle everything, but to build a layered, collaborative model system: general models handle semantic understanding and high-level planning, while specialized models handle state estimation, action generation, contact judgment, and real-time control.

4. Specialized perception such as touch will become an important supplement

Vision is a primary means for robots to obtain environmental information, but vision alone often cannot determine whether an object has truly been grasped stably, nor can it accurately identify hardness, surface friction, and contact pressure.

Tactile information can make up for these shortcomings. For example, when grasping fragile objects, a robot needs to know whether contact has occurred, whether contact pressure is even, and whether the object is slipping. In insertion, screwing, or assembly tasks, the robot also needs to perceive subtle resistance changes and contact deviations.

The value of tactile models is not just converting sensor data into a label. More importantly, they should form representations that can be used for decision-making. An ideal tactile representation should be able to answer:

• Whether effective contact is currently occurring;
• Whether the contact position is stable;
• Whether the object shows a tendency to slip;
• Whether the current force is suitable for continuing the operation;
• Whether the next step should maintain, adjust, or withdraw the action.

Such capabilities usually require dedicated data collection, temporal modeling, and task training. They are not a substitute for general vision-language models. Instead, they jointly form the perceptual foundation through which robots understand the physical world.

5. The value of world models lies in predicting the consequences of actions

Robots cannot only react to the current image. They also need to predict the possible results of actions. For example, pushing an object may cause it to fall out of view, opening a drawer may change the positions of surrounding objects, and moving a container may cause liquid inside to slosh.

World models for physical interaction aim to learn the relationship between environment states, actions, and results. They can help robots simulate and compare before executing an action:

• Which path is safer;
• Which grasp point is more stable;
• Whether the current action will occlude future targets;
• How to correct the next step if the first attempt fails;
• Whether the task has reached completion conditions.

However, world models also cannot simply be regarded as the final answer. Environmental changes, object materials, and contact relationships are highly complex, and model predictions will inevitably contain errors. A practical system needs to combine predictive models with real-time sensor feedback to form a predict-execute-correct mechanism, rather than relying entirely on offline simulation.

6. The core value of robot-specific models is closeness to the task and the body

The fact that robots need their own models does not mean building a system completely independent of general-purpose large models from scratch. A more reasonable approach is to use the knowledge and reasoning capabilities of general models and then adapt them to a specific robot.

Specialized models should focus on at least the following aspects:

6.1 Adapt to the robot’s body structure

Different robots have different degrees of freedom, end effectors, sensor layouts, and motion ranges. A model needs to understand its own capability boundaries and avoid generating actions that cannot be executed.

6.2 Adapt to the specific working environment

Warehouses, homes, laboratories, and production sites differ significantly in object types, spatial layouts, and safety requirements. Models should learn environmental patterns from scene data and task data.

6.3 Learn action outcomes

Robot training should not only record what was done. It should also record what happened after the action. Whether the action succeeded, whether the object was damaged, whether the path was stable, and whether contact was abnormal should all become important bases for training and evaluation.

6.4 Support failure recovery

Failure is unavoidable in open environments. An excellent robot model should not only have a high success rate. It should also be able to identify the cause of failure and take measures such as relocalizing, adjusting force, changing the grasp point, or requesting human intervention.

7. Data closed loops will determine the deployment speed of embodied AI

The development of general models depends on large-scale data. Embodied AI also needs data, but robot data is more expensive to obtain. Each data sample usually contains information such as vision, actions, poses, force sensing, touch, environment state, and task results. The collection process also involves hardware runtime and safety costs.

Embodied AI therefore needs a complete data closed loop:

1. Collect data: record multimodal sensors, action trajectories, and task goals;
2. Clean data: remove sensor anomalies, interrupted actions, and samples with incomplete annotations;
3. Construct task labels: add information such as success, failure, risk, and recovery strategies;
4. Train models: learn scene understanding, action generation, and outcome prediction;
5. Deploy and validate: test in simulation and real environments;
6. Feed back failure cases: bring abnormal situations back into training and evaluation.

Compared with simply increasing model scale, continuously accumulating high-quality robot interaction data may be more decisive for system reliability in real tasks.

8. The future system form will be multi-model collaboration, not single-model replacement

From an engineering perspective, an embodied AI system may consist of multiple layers:

• Interaction layer: understand natural language and user intent;
• Task layer: decompose goals, arrange steps, and manage task states;
• Scene layer: fuse vision, touch, position, and historical information;
• Action layer: generate actions that comply with robot constraints;
• Control layer: perform real-time trajectory tracking, obstacle avoidance, and force control;
• Safety layer: monitor risks, trigger emergency stops, and manage permissions.

General-purpose large models will mainly enhance the interaction layer and task layer, while specialized embodied models will focus on the scene layer, action layer, and control layer. The layers will be connected through explicit state representations and constraint interfaces, which can leverage the generalization ability of large models while maintaining the stability of low-level execution.

This architecture also makes debugging and validation easier. When a task fails, engineers can determine whether the problem comes from goal understanding, scene recognition, action planning, or control execution, instead of facing an unexplainable monolithic model.

9. Embodied AI evaluation cannot rely only on demonstrations

Robot system evaluation should expand from “can it complete the task” to “is it stable, safe, and transferable?”

Key metrics include:

• Task success rate;
• Stability during continuous execution;
• Adaptability to environmental changes;
• Recovery capability after failure;
• Action latency and resource consumption;
• Incidence of collisions, incorrect operations, and abnormal actions;
• Generalization to new objects, new positions, and new instructions;
• Explainability and controllability during human-robot collaboration.

In particular, it is important to avoid showing only a small number of successful cases. Real deployment pays more attention to average performance during long-term operation and whether the system remains cautious when facing unknown situations.

Conclusion: Large models will change embodied AI, but they will not eliminate robot-specific technology

The continued development of general-purpose large models will significantly improve robots’ ability to understand tasks, organize steps, handle complex instructions, and adapt to new scenes. It will push robots from “executing programs” toward “understanding goals and completing them autonomously,” and it will lower the barriers to interaction and task programming.

However, the physical world imposes higher requirements for real-time performance, reliability, safety, and contact perception. Robots still need specialized models built for their own structures, sensors, action spaces, and working environments. The truly competitive solutions of the future will not simply move digital models onto robots. They will organically combine general knowledge, embodied perception, action prediction, real-time control, and safety mechanisms.

In other words, general-purpose large models provide a higher cognitive ceiling, while robot-specific models determine whether the system can reliably translate that cognition into effective action in the real world. The ultimate breakthrough in embodied AI will come from long-term, continuous, and verifiable collaboration between the two.