How General-Purpose Foundation Models Will Reshape Embodied Intelligence

1. Embodied Intelligence Is Entering an Era of General-Purpose Cognition

In the past, embodied intelligence systems typically followed highly specialized technical paths. Data was collected for a particular robot, sensor configuration, and task, after which a dedicated policy model was trained. These systems could perform well in controlled environments, but even small changes—such as different object locations, lighting conditions, or tool specifications—often required new data collection or retraining.

The development of general-purpose foundation models is changing this approach. These models offer stronger transfer capabilities in natural-language understanding, image analysis, spatial reasoning, step-by-step planning, and code generation. They may not directly replace low-level controllers, but they can serve as the cognitive center of a robotic system, responsible for understanding goals, decomposing procedures, invoking tools, interpreting anomalies, and adjusting strategies.

As a result, the core competitiveness of embodied intelligence may depend less on the accuracy of a single action-policy model and more on whether the overall system can reliably connect general-purpose cognition to the physical world.

2. In-Situ Learning May Be the Most Immediate Impact

Traditional robots usually execute predefined procedures. For example, a system may first move to an object, then grasp it, and finally transfer it to a designated area. If the environment changes slightly, the predefined sequence may fail.

More advanced general-purpose models can adjust their behavior based on visual input and execution feedback. Consider a dual-arm robot performing everyday tasks such as moving food, installing small consumables, opening containers, or organizing objects. During execution, it may encounter situations such as:

1. The target object is blocked by other objects.
2. The gripper does not make accurate contact.
3. The object slips or changes orientation.
4. There is insufficient operating space.
5. Sensor observations do not match expectations.

In traditional systems, these situations usually require engineers to write exception branches in advance. A general-purpose model can first interpret the current state and then replan the next action. For example, it may determine that the current grasping posture is unsuitable for insertion, release the object, rotate it, and try a different path.

In-situ learning does not necessarily mean that the model immediately modifies its own parameters. More practical implementations include:

• Regenerating a task plan based on current observations;
• Retrieving similar cases from historical operation records;
• Adjusting the order of actions and control objectives;
• Validating candidate solutions in a simulation environment;
• Recording failure causes for later strategy optimization.

Therefore, in-situ learning will initially appear mainly as context-based adaptation and strategy correction during execution, rather than unrestricted online training.

3. World Models May Become Part of General-Purpose Models

Embodied intelligence has long faced a fundamental challenge: a robot must not only recognize what is in front of it, but also predict what will happen after taking a particular action.

When a robot sees a cup, for example, it must do more than classify it as a cylindrical object. It also needs to estimate:

• Which direction provides the most stable grasp;
• Whether the grasp will collide with nearby objects;
• Whether the cup contains liquid;
• How much force is needed when placing it;
• Whether sufficient space will remain after the object is moved.

This ability to predict environmental states, action outcomes, and future changes is commonly referred to as world-model capability.

In the past, world models were often built separately for specific scenarios. General-purpose models have an advantage because they have learned broad patterns about objects, actions, and events from large-scale visual, language, and video data. As their temporal understanding improves, they may provide robots with initial environmental predictions and task reasoning without requiring fully specialized training.

However, general knowledge should not be treated as an accurate physical model. Real robots must deal with friction, deformable objects, collision risks, weight differences, and sensor errors. A foundation model can propose candidate solutions, but final actions still need to be validated by motion planning, force control, visual servoing, and safety constraints.

A more practical technical path is a layered architecture rather than a single model handling every function:

• General-purpose model: Understands goals, semantics, and the environment;
• World-state module: Maintains objects, positions, relationships, and task progress;
• Planning module: Generates executable action sequences;
• Control module: Converts actions into continuous control signals;
• Safety module: Limits speed, force, range, and hazardous behaviors;
• Feedback module: Evaluates execution results and triggers replanning.

4. Robot Control Will Shift from Action Scripts to Goal-Driven Execution

Traditional automation systems are usually built around fixed scripts. They work well in stable industrial processes with standardized workpieces. As general-purpose models enter the picture, robot control may gradually shift toward a goal-driven model.

Users will no longer need to specify every individual action. Instead, they can provide higher-level goals, such as placing a specified object into a heating device, sorting loose components by category, or completing a step in an experiment. The system can then break the goal into executable subtasks based on visual information, tool status, and environmental constraints.

This approach offers three main benefits.

1. Lower Task-Configuration Costs

Companies will not need to write a new control script for every minor variation. Systems may be configured through natural language, demonstration motions, or a small number of examples.

2. Better Transfer Across Tasks

The same cognitive system could support material handling, sorting, assembly, laboratory assistance, and household services. The underlying robot would only need to provide the appropriate tools and execution capabilities.

3. Improved Exception Handling

The system can explain failure causes based on the current state instead of simply returning “execution failed.” This can help maintenance personnel determine whether the problem occurred in visual recognition, path planning, grasping posture, or mechanical execution.

Goal-driven control does not eliminate engineering constraints. High-risk tasks require permission levels and action-approval mechanisms. When operations involve high temperatures, high pressure, sharp tools, chemicals, or close human-robot collaboration, model-generated plans must pass rule-based validation and may require human confirmation.

5. Implications for Embodied Intelligence Companies: Higher Barriers for Small Teams

As general-purpose model capabilities improve, the embodied intelligence industry may undergo significant structural changes.

On one hand, basic capabilities will gradually become platformized. Visual understanding, language interaction, task decomposition, code generation, and general-purpose planning will not need to be trained from scratch by every company. Businesses can focus more on robot bodies, data collection, industry workflows, and safety validation.

On the other hand, business models based solely on “one specialized model for one small scenario” may face increasing pressure. If general-purpose models can cover a substantial portion of perception and planning, products without distinctive data, hardware capabilities, or industry barriers may have less room to differentiate.

The companies that remain competitive may need to combine several capabilities:

• Stable robot hardware and actuation mechanisms;
• A high-quality, multimodal, traceable data loop;
• Safety testing in real-world environments;
• Reliable integration between general-purpose models and low-level control systems;
• Workflow advantages in manufacturing, logistics, healthcare assistance, scientific research, or other sectors;
• Continuous evaluation and failure-replay mechanisms.

In other words, models themselves will become more important, but system-engineering capabilities across models, robots, and application scenarios will matter even more.

6. Data Loops Will Determine Deployment Success

Embodied intelligence differs from purely software-based applications. Every robot operation produces rich multimodal data, including video, depth information, joint states, end-effector poses, tactile feedback, voice instructions, action trajectories, and task outcomes.

This data should not simply be accumulated. It needs to form a complete loop:

1. Collect real-world tasks and failure cases;
2. Classify and label anomalies;
3. Analyze the specific stage where the failure occurred;
4. Reproduce the problem in a simulation environment;
5. Optimize planning, control, or safety strategies;
6. Validate the changes in the real environment;
7. Incorporate the results into the next round of training and evaluation.

Failure data is particularly valuable. Successful cases show that a solution can work, while failures reveal the boundary conditions under which the system breaks down. For robots, occlusion, reflections, deformable objects, object adhesion, and cramped spaces may determine practical usability more than standard samples do.

Companies should also establish clear evaluation metrics rather than focusing only on task completion rates. Other important measures include:

• Average time per task;
• Number of retries;
• Probability of incorrect operations;
• Adaptability to environmental changes;
• Ability to handle unfamiliar objects;
• Safe distance during human-robot collaboration;
• Stability during long-running operation;
• Cost of recovery after an error.

7. General-Purpose Models Still Have Clear Boundaries

Although general-purpose models may significantly improve embodied intelligence, they cannot solve every problem automatically.

First, model outputs are uncertain. A plan that sounds reasonable in language may not correspond to a physically executable action. Second, visual understanding can be affected by occlusion, lighting, and viewpoint. Third, models generally lack direct access to precise mechanical parameters and cannot determine from common sense alone whether a force will damage an object.

Robot execution is also constrained by hardware, including joint degrees of freedom, payload capacity, response speed, sensor accuracy, and end-effector design. Even when a model generates a correct plan, the hardware may be unable to execute it.

For this reason, embodied intelligence systems need to combine model capabilities with deterministic constraints:

• Use foundation models for high-level tasks;
• Use verifiable planning algorithms for geometric paths;
• Use real-time controllers for critical actions;
• Use hard constraints to block hazardous operations;
• When the state is uncertain, prioritize observation, pausing, or human confirmation.

8. The Future Is More Likely to Be a Collaborative Architecture Than a Single Model

General-purpose models will play an increasingly important role in embodied intelligence, but they are more likely to form a multi-layer collaboration architecture than to replace every specialized component.

General-purpose models are well suited to semantic understanding, goal decomposition, and cross-task transfer in open-world environments. Specialized models are better suited to high-frequency, low-latency perception and real-time control. Traditional algorithms remain valuable for explainable and verifiable geometric planning and safety constraints.

A mature system may resemble a “cognitive operating system.” The upper layer understands people and the environment, the middle layer handles planning and scheduling, and the lower layer manages control and safety. Stronger model capabilities can expand the range of tasks the system can handle, while better engineering constraints improve reliability in real-world environments.

Conclusion

The most important impact of next-generation general-purpose models on embodied intelligence is not simply enabling robots to understand more instructions. It is giving them stronger task understanding, in-situ adaptation, error correction, and cross-scenario transfer.

These models may lower the development barrier for robot applications and reduce duplicated effort, while shifting competition away from isolated model performance toward a broader combination of data, hardware, application scenarios, and system reliability.

However, moving from laboratory capabilities to large-scale commercial deployment still requires solutions for real-time performance, precise control, data loops, safety validation, and cost management. The breakthrough will not come from a single model. It will come from stable collaboration between general-purpose cognition, robotic execution systems, industry data, and engineering constraints.