Large Language Models for Robotic Arm Control: Progress and Challenges in Embodied AI Evaluation

1. Why Evaluate Large Language Models in the Physical World?

Most large language model evaluations have traditionally focused on text-based tasks such as language understanding, code generation, and mathematical reasoning. These benchmarks have helped drive steady improvements, but they are also approaching a practical ceiling. As model scores on static questions begin to saturate, those scores reveal less about whether a model can complete useful real-world tasks.

The physical world provides a more demanding and realistic test environment for three reasons:

1. Closed-loop operation: A text task often involves producing a single answer. Robotic manipulation requires a continuous perception-planning-execution-verification loop. An error at any stage can propagate through the rest of the task.
2. Irreversible consequences: A failed grasp can cause an object to fall, while incorrect placement can make later steps impossible. Errors have physical consequences, so the model must act cautiously and recover when something goes wrong.
3. Real-time and cost constraints: Physical hardware incurs costs through operating time, model inference, and potential failures. Efficiency and cost therefore become as important as task accuracy.

A recent research-preview benchmark from an AI evaluation organization placed frontier large language models in this type of environment. Instead of directly generating joint torques, each model wrote code to control a six-axis robotic arm. The benchmark included eight tabletop tasks involving actions such as grasping objects, arranging items, and pressing buttons.

In the initial results, the highest-ranked frontier model achieved an overall score of approximately 69 points. However, it required about 30 minutes per task on average, while a human completed the same task in roughly 20 seconds. That is a speed difference of nearly two orders of magnitude. Even the model with the best cost performance averaged approximately $1.93 per task.

These results offer a practical snapshot of how well current large language models can interact with the physical world.

2. Benchmark Design: Code as the Action Interface

2.1 Why Use Code to Control a Robot?

There are three common ways to connect an AI model to a robotic system:

• Low-level control: The model directly generates sequences of joint angles or torques. This requires extremely high spatial and temporal precision and remains difficult to execute reliably.
• End-to-end policies: A multimodal model maps visual input directly to actions. This shortens the control pipeline but makes the system harder to interpret and audit.
• Code as action: The model reads task instructions and perception data, then generates executable control code that calls a set of predefined robot APIs.

The benchmark used the third approach. This is not simply a compromise. Code provides several useful properties:

• Composability: Models can use loops, conditional logic, functions, and reusable routines.
• Auditability: Generated programs can be statically checked before execution.
• Tool integration: Visual queries, motion planning, and gripper control can all be exposed as ordinary function calls.

For developers, this approach also resembles a realistic engineering architecture in which an AI agent coordinates an existing toolchain instead of replacing every layer of the control stack.

2.2 Three Capability Dimensions

The eight tabletop tasks were designed to cover three progressively more demanding capability categories:

1. Control and dexterous manipulation: Grasping objects with different shapes and materials tests motion precision and force control.
2. Spatial reasoning: Understanding relative object positions is necessary for placement, nesting, and ordered operations.
3. Adaptability: When object positions or task conditions change, the model must replan and resume execution.

This structure makes benchmark scores easier to diagnose. A failure can indicate that the model cannot grasp reliably, cannot reason about the scene, or cannot adapt when the environment changes.

3. Interpreting the Results: Basic Capability Exists, but the Efficiency Gap Remains

The published results support several conclusions.

Capability

A leading score of about 69 suggests that a language model can understand physical tasks and complete at least some of them by generating control code. The overall approach is therefore viable.

However, a gap of roughly 30 points remains. A meaningful proportion of attempts still end in complete or partial failure, so current systems are far from the reliability required for dependable deployment.

Competitive Landscape

Several frontier models achieved relatively close scores. This suggests that current embodied capabilities are still largely determined by the general generation of the underlying foundation model. Robot-specific optimization has not yet created a decisive performance gap.

Efficiency

The difference between approximately 30 minutes for a model and 20 seconds for a human cannot be explained simply by saying that the model is slow or unintelligent. The latency and cost come from multiple layers of the execution pipeline.

4. Where the 30-Minute Runtime and $1.93 Cost Come From

4.1 Repeated Perception and Reasoning Calls

A typical code-driven control loop looks like this:

1. Observe the scene.
2. Reason about the next action.
3. Execute the action.
4. Observe again to verify the result.

Every observation and reasoning step may require another multimodal model call. Image inputs can also increase context size significantly.

A single grasp might involve four separate stages:

• Locate the object.
• Move to a pre-grasp position.
• Close the gripper.
• Confirm that the grasp succeeded.

This sequence can require four or more rounds of model inference.

4.2 Serial Physical Execution

Physical manipulation is inherently difficult to parallelize. A robotic arm must move through actions in sequence, and safety-related speed limits cannot be ignored.

Even an instantaneous model could not eliminate the arm’s physical movement time. When the model itself introduces additional latency, it extends a workflow that is already sequential.

4.3 Retries and Error Recovery

Adaptability often depends on retries. If the arm misses an object, it must retreat and try again. If an item is placed incorrectly, the system may need to reposition it.

Each retry repeats perception, reasoning, and execution. As the failure rate increases, average runtime and cost can grow by multiples rather than by a small fixed amount.

4.4 Context Growth in Long-Horizon Tasks

During a multistep task, the execution history continues to accumulate. This history may include generated code, observations, execution results, and error messages.

Inference cost can grow approximately linearly—or even superlinearly—with the number of interaction rounds. This also helps explain why the highest-scoring model and the lowest-cost model are often different.

A stronger model may spend more resources reasoning, while a cheaper model may reason less but require more retries. Both patterns create efficiency costs in different ways.

5. Reusable Principles for Embodied AI Benchmark Design

Teams building their own evaluations for embodied AI can apply six general principles from this benchmark.

1. Create Task Difficulty Levels

Design a progression based on control precision, spatial complexity, and disturbance intensity. A single undifferentiated task set makes it difficult to identify why one system outperforms another.

2. Use Multiple Metrics

Report at least three dimensions:

• Success rate
• Average completion time
• Cost per task

Any single metric can lead to misleading engineering decisions. A model with a high success rate may be too slow or expensive, while an inexpensive model may fail too often to be useful.

3. Make Results Reproducible

Use fixed hardware and standardized calibration procedures. Randomize initial object poses, but record the random seed so that experiments can be repeated.

4. Prevent Overfitting

Maintain a private holdout task set. If all tasks are public, developers may optimize specifically for known scenarios instead of improving general robotic capabilities.

5. Define Safety Boundaries

The execution environment should enforce workspace limits, maximum speeds, and torque protection. A robotics benchmark must not become a destructive hardware experiment.

6. Preserve a Complete Execution Trace

Record every observation, generated program, execution result, and exception. Complete traces are essential for identifying whether a failure originated in perception, planning, code generation, motion execution, or verification.

6. A Minimum Viable Execution Pipeline

A minimal architecture for reproducing this type of LLM-controlled robotic workflow contains five modules:

1. Camera-based perception
2. Workspace calibration
3. A robot API with built-in safety limits
4. A sandbox for generated code
5. Result verification and logging

The following pseudocode illustrates the interface structure. It is not based on any specific commercial product:

MAX_SPEED = 0.5  # Example safety limit; configure according to the hardware specifications
class RobotSession:
    """A thin wrapper around physical hardware with built-in safety checks."""
    def __init__(self, arm, gripper, camera):
        self.arm, self.gripper, self.camera = arm, gripper, camera
    def locate(self, obj_name: str) -> tuple:
        # Return the object's (x, y, z) coordinates in the workspace frame
        return self.camera.detect(obj_name)
    def move_to(self, x: float, y: float, z: float, speed: float = 0.2):
        assert self._in_workspace(x, y, z), "Position is outside the safe workspace"
        self.arm.move_linear([x, y, z], speed=min(speed, MAX_SPEED))
    def grasp(self, width: float):
        self.gripper.close_to(width)
    def verify(self, expect: str) -> bool:
        return self.camera.check_state(expect)
def run_task(session: RobotSession, plan_code: str, max_retry: int = 3):
    for attempt in range(max_retry):
        exec(plan_code, {"robot": session})  # Execute model-generated code in a sandbox
        if session.verify("task_done"):
            return True
    return False

Three engineering details are especially important.

Use Semantic APIs

The abstraction layer should expose semantic operations such as locating, approaching, grasping, and placing objects. It should not require the model to generate raw joint commands.

Semantic APIs reduce the probability of errors and provide a convenient point for enforcing safety policies.

Execute Generated Code in a Sandbox

Model-generated code must pass static checks and run in a restricted environment. The system must prevent out-of-bounds motion and dangerous instructions before they reach the physical hardware.

Retain Observations and Logs

Each interaction round should store screenshots, generated code, and execution logs in a unified logging service and object storage system. Monitoring and alerting can then track changes in success rate, latency, and failure patterns.

This pipeline does not require unusual infrastructure. Standard cloud capabilities—including container platforms, object storage, message queues, logging, monitoring, and alerting—are sufficient to support it.

7. From Evaluation to Deployment: A Practical Path

The benchmark results suggest a staged approach to deploying LLM-driven robotic systems.

What Is Practical Today

Use a layered architecture. The large language model should handle task-level planning, code generation, and exception handling. Traditional motion control and visual servoing should manage low-level execution.

This design removes high-frequency control from the slow inference loop. The underlying controller determines the physical cycle time, while the language model acts as the system’s high-level planner rather than its low-level motor controller.

Suitable Use Cases

Current systems are better suited to low-frequency, highly flexible tasks that can tolerate response times of several seconds or longer. Examples include:

• Laboratory sample handling
• Small-batch flexible sorting
• Education
• Prototype validation

These applications are less sensitive to speed and can benefit from the generalization capabilities of language models.

Use Cases That Should Wait

High-throughput production lines and safety-critical processes are not yet suitable targets for direct replacement. When speed and cost remain separated from human performance by approximately two orders of magnitude, the economics and safety case are difficult to justify.

Medium-Term Directions

Three areas are particularly important:

1. Model distillation and reduction: Compress the planning capabilities of large models into smaller policy models that can run at higher inference frequencies.
2. Simulation-first training: Conduct large-scale training in simulation before transferring policies to physical hardware. Abundant simulation time can offset the high cost of real-world robot operation.
3. More granular efficiency metrics: Evolve from measuring minutes per task to measuring seconds per action, making it easier to identify specific sources of latency.

8. Conclusion

The most important contribution of this embodied AI benchmark is not the ranking itself. Its value lies in turning the question of whether a model can act in the physical world into a measurable, reproducible, and traceable engineering problem.

A score of approximately 69 shows that the basic approach works. Operating at roughly one-hundredth of human speed, with per-task costs measured in dollars, shows how much work remains.

Rather than focusing only on leaderboard positions, developers should study the benchmark design and the causes of failure, latency, and cost. A small validation pipeline in a controlled environment can then provide the data needed to answer the most practical question: Is this task ready to be placed in the model’s hands today?