Assessing Embodied AI Models: Evaluating Capabilities and Practical ROI

The competition among multimodal large models is shifting from “understanding text and images” to “executing continuous actions based on visual information.” This means models must not only recognize objects and understand instructions, but also control robotic arms to grasp, twist, transport, and assemble by integrating spatial relationships, physical laws, and operational feedback. Recently, a new reasoning model published its robot control test results, sparking industry discussions about its practical capabilities and return on investment (ROI). Publicly, this model achieved an average score of 64.9% in a dual-arm robot simulation test, significantly outperforming its predecessor (27.6%), a high-end model (39.4%), and a general frontier model (29.8%). While the absolute numbers show the new model more than doubled the previous generation’s score, understanding what these numbers mean is more important than the numbers themselves.

1. What Does Robot Control Testing Actually Evaluate?

Robot control models must complete a full closed-loop pipeline:

1. Receive camera or sensor input;
2. Identify objects, positions, and states in the scene;
3. Understand natural language or task goals;
4. Formulate an action plan;
5. Convert the plan into robot control commands;
6. Correct actions based on execution results;
7. Maintain task goals without deviation during multi-step operations.

Traditional vision models might only identify “a bottle on the table.” Embodied AI models must also determine the rotation direction of the bottle cap, the contact point of the gripper, the applied force, and the sequence of actions. If an initial attempt fails, the model should adjust its strategy based on new visual feedback rather than repeating the same error.

Therefore, robot evaluation is not simple image classification or one-shot action prediction; it is a comprehensive assessment of visual understanding, spatial reasoning, planning ability, control stability, and error recovery.

2. How to Interpret the 64.9% Average Score

First, an average score is not equivalent to a task success rate. If a test includes multiple tasks scored on metrics like approach to target, action completion, trajectory quality, and final state, a model might fully succeed in some tasks and only partially complete others, resulting in an average score. Thus, 64.9% more accurately means the model demonstrated high overall completion quality under the specific test set and scoring criteria—not that “64.9% of tasks were completed in a single attempt.”

Second, scores heavily depend on test conditions. Results are influenced by whether models use identical input formats, control interfaces, prompt templates, action frequencies, maximum inference times, and retry strategies. Even if a unified execution framework allows each model to use its maximum inference strength, making results somewhat comparable, this still does not equate to performance in all real-world deployment scenarios.

Third, success in a simulation environment does not guarantee stable success on a real robot. Simulations typically provide cleaner images, more accurate object states, and more deterministic physical feedback. Real-world environments present lighting variations, occlusions, friction differences, mechanical errors, latency, and sensor noise. Transferring a model from simulation to reality requires overcoming a significant “sim-to-real” gap.

3. Comparisons Under the Same Framework Are Valuable but Require Caution

The most informative aspect of these results is not the absolute ranking of a single model, but the relative differences among multiple models under the same testing framework.

The new model’s improvement over its predecessor suggests enhancements in the following areas:

• More stable multi-step task planning;
• More accurate understanding of spatial relationships and object states;
• The ability to correct intermediate actions using visual feedback;
• Better goal retention in longer task chains;
• Stronger transferability to tasks not explicitly trained on.

However, public information did not disclose detailed scores for each sub-task, making it impossible to confirm where the improvements were greatest. For instance, the model might have significantly improved in pick-and-place tasks but still struggles with fine insertion, flexible object manipulation, or high-precision assembly. Without itemized data, it is unclear whether the model’s capabilities improved across the board or if the average was pulled up by a few dominant tasks.

Additionally, discrepancies may exist between internal test results and official leaderboard scores. Even within the same test environment, variations in hardware configuration, control frequency, prompting, random seeds, retry mechanisms, and result aggregation can cause score fluctuations. Enterprises should clearly label results as either “internal test results” or “unified leaderboard results” to avoid conflating the two.

4. What Real Robot Demonstrations Prove

Beyond simulation scores, the developers demonstrated the model controlling physical robots to perform tasks like solving puzzle cubes, twisting bottle caps, and using tools to draw patterns.

These demonstrations show that the model has entered the real-environment closed-loop control phase, rather than just offline trajectory prediction or simulation. Real robot tasks typically require handling the following issues:

• Misalignment between camera perspective and object position;
• Control errors in robot joints and end effectors;
• Difficulty in fully pre-modeling object material, friction, and weight;
• Occlusions and state changes during execution;
• Collision risks from fast movements and efficiency losses from slow movements;
• Re-observation and re-planning after failures.

If a model can complete these operations without specific training for the exact task, it indicates a degree of task generalization. However, “not explicitly trained” does not mean the model has never encountered similar visual concepts, manipulation knowledge, or public video data. A more rigorous evaluation would require the tester to disclose task construction methods, training data boundaries, success criteria, number of attempts, and failure cases.

A demo video proves that “one execution was successful,” but it cannot independently prove the model’s stability across numerous random scenarios. For engineering applications, more critical metrics include continuous operational success rate, average action latency, failure recovery rate, error probability, and performance fluctuations across different environments.

5. How to Judge Practical ROI

Evaluating an embodied AI model requires looking beyond accuracy to establish a comprehensive cost model.

1. Task Success Rate

Success rate is the most direct metric, but step-level success should be distinguished from long-chain success. If a task involves 10 critical steps, each with a 95% success rate, the overall success rate in an ideal independent scenario is:

0.95^10 ≈ 59.9%

This shows that while single-step performance seems excellent, overall success can drop significantly after multi-step accumulation. Long tasks must prioritize state memory, error detection, and recovery capabilities.

2. Inference Latency

High-intensity inference generally yields better planning quality but increases response time. In scenarios like warehouse sorting or industrial assembly, latency affects throughput and can cause safety issues. Enterprises must differentiate between a “high-quality mode for offline planning” and a “low-latency mode for real-time control,” rather than routing all tasks to the highest inference intensity.

3. Call and Deployment Costs

Model costs extend beyond per-call fees to include:

• Compute overhead from image and video inputs;
• Cumulative costs from high-frequency calls during continuous control;
• Infrastructure costs for local inference devices and network environments;
• Costs for logging, monitoring, data replay, and fault diagnosis;
• Safety auditing and manual takeover costs.

If a model requires long inference times for every action, high overall operational costs may negate its business value despite a high task success rate.

4. Engineering Integration Difficulty

A model’s ease of integration into existing systems also affects its ROI. Key considerations include whether it can stably output structured actions, support clear stop conditions, restrict dangerous actions, easily connect to sensors and robot controllers, and retain complete decision traces during exceptions.

In practice, a layered architecture is recommended: “the model handles high-level decisions, while traditional controllers handle low-level execution.” The model outputs targets, action sequences, or constraints, and the underlying control system handles trajectory planning, collision detection, speed limiting, and emergency stops. This reduces the risks of occasional model errors.

6. Capabilities Needed to Move from Simulation to Production

Transitioning embodied AI from test demos to production environments requires at least four types of validation.

First, environmental generalization validation. Repeat tests under different lighting, backgrounds, object placements, and camera angles to ensure the model is not overfitted to a single fixed scene.

Second, disturbance and exception validation. Artificially introduce occlusions, object sliding, grasping failures, missing targets, and sensor noise to observe if the model can detect issues and take safe actions.

Third, long-term stability validation. A single success in a short video is insufficient to prove the system can work for hours. Enterprises should monitor performance decay, resource usage, network fluctuations, and error accumulation during continuous tasks.

Fourth, safety boundary validation. Robotic systems must have motion limits, area isolation, collision detection, manual takeover, and emergency stop mechanisms. No large model should directly possess unconstrained low-level execution permissions.

7. How Developers and Enterprises Should Design Evaluation Processes

Adopt a tiered evaluation approach rather than directly connecting the model to a real production line.

Phase 1: Offline data evaluation. Use a fixed task set to check visual understanding, target recognition, and action planning capabilities.

Phase 2: Simulation closed-loop evaluation. Test generalization and recovery capabilities through randomized scenes and disturbance conditions.

Phase 3: Semi-physical testing. Allow the model to control real actuators but restrict motion range, speed, and work area to safe limits.

Phase 4: Small-scale trial runs. Record success rates, latency, manual takeover frequency, and exception types.

Phase 5: Production environment deployment. Establish a continuous data feedback mechanism to discover new failure modes and optimize task workflows.

At the implementation level, save input images, model decisions, control commands, execution results, and exception logs for every task. This data creates a traceable pipeline to determine whether issues originate from visual recognition, task planning, control execution, or hardware status.

Conclusion

The significant improvements of new multimodal reasoning models in robot control tests indicate that general models are gaining stronger visual understanding, task planning, and action generalization capabilities. The clear progress over previous models under a unified testing framework holds technical reference value.

However, a single average score and a few demonstrations do not equate to mature industrial capability. The true determinants of practical value are the model’s stability in complex environments, long-chain task success rate, real-time response capability, deployment cost, safety mechanisms, and failure recovery.

Therefore, when judging ROI, do not merely ask “is the score higher.” Instead, ask: Are these results reproducible? Do they cover real business scenarios? Can the system recover from failures? What is the cost per task? Can it safely integrate with existing control systems? Only when model capability, engineering reliability, and operational costs reach equilibrium can embodied AI truly transition from technical demos to large-scale applications.