Recognition: 2 theorem links
· Lean TheoremEmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents
Pith reviewed 2026-05-17 00:20 UTC · model grok-4.3
The pith
MLLMs excel at high-level embodied tasks but score only 28.9 percent on low-level manipulation.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
EmbodiedBench demonstrates that MLLMs perform better on high-level semantic tasks such as household activities but achieve only low success rates on low-level tasks that require atomic actions including navigation and manipulation, with GPT-4o reaching the highest average score of 28.9 percent across all evaluated settings.
What carries the argument
EmbodiedBench, a benchmark of 1128 tasks distributed across four environments and six capability subsets that separately measure commonsense reasoning, complex instruction understanding, spatial awareness, visual perception, and long-term planning.
If this is right
- MLLMs need targeted improvements in linking visual input to precise motor commands for manipulation success.
- Long-term planning and spatial awareness remain bottlenecks that limit overall agent reliability.
- The six capability subsets provide a diagnostic tool for developers to isolate and fix specific weaknesses.
- Standardized testing across high-level and low-level tasks can track whether new models close the observed performance gap.
Where Pith is reading between the lines
- If simulation results hold in physical settings, then pure scaling of current MLLMs will not produce capable embodied agents without new training methods that include action feedback.
- The benchmark could be extended by adding physics-based noise or real-robot transfer tests to check whether low-level failures stem from simulation artifacts.
- Hybrid systems that combine MLLMs with separate low-level controllers might bypass the manipulation weakness identified here.
Load-bearing premise
The chosen simulated environments and six capability subsets capture the main difficulties that embodied agents would face outside simulation.
What would settle it
A model that reaches above 60 percent success on the low-level manipulation and navigation subsets while preserving high scores on the semantic and planning subsets would falsify the claim of inherent struggle with low-level tasks.
read the original abstract
Leveraging Multi-modal Large Language Models (MLLMs) to create embodied agents offers a promising avenue for tackling real-world tasks. While language-centric embodied agents have garnered substantial attention, MLLM-based embodied agents remain underexplored due to the lack of comprehensive evaluation frameworks. To bridge this gap, we introduce EmbodiedBench, an extensive benchmark designed to evaluate vision-driven embodied agents. EmbodiedBench features: (1) a diverse set of 1,128 testing tasks across four environments, ranging from high-level semantic tasks (e.g., household) to low-level tasks involving atomic actions (e.g., navigation and manipulation); and (2) six meticulously curated subsets evaluating essential agent capabilities like commonsense reasoning, complex instruction understanding, spatial awareness, visual perception, and long-term planning. Through extensive experiments, we evaluated 24 leading proprietary and open-source MLLMs within EmbodiedBench. Our findings reveal that: MLLMs excel at high-level tasks but struggle with low-level manipulation, with the best model, GPT-4o, scoring only 28.9\% on average. EmbodiedBench provides a multifaceted standardized evaluation platform that not only highlights existing challenges but also offers valuable insights to advance MLLM-based embodied agents. Our code and dataset are available at https://embodiedbench.github.io.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces EmbodiedBench, a benchmark for vision-driven embodied agents powered by multi-modal large language models (MLLMs). It comprises 1,128 tasks distributed across four simulated environments (spanning high-level household semantics to low-level atomic actions in navigation and manipulation) together with six curated capability subsets that isolate commonsense reasoning, complex instruction following, spatial awareness, visual perception, and long-term planning. Experiments evaluate 24 proprietary and open-source MLLMs; the central empirical finding is that current models handle high-level tasks reasonably well but fail on low-level manipulation, with the strongest model (GPT-4o) reaching only 28.9 % average success.
Significance. If the reported scores are reproducible, the work supplies the first large-scale, standardized empirical map of MLLM limitations in embodied settings. The public release of code, dataset, and evaluation harness is a concrete strength that supports reproducibility and incremental progress. The differentiation between high-level semantic success and low-level control failure supplies actionable guidance for future model and training improvements.
minor comments (3)
- [§3] The abstract states that the four environments and six subsets were 'meticulously curated' but does not list the explicit selection criteria; a short paragraph in §3 or §4 that enumerates the coverage goals and exclusion rules would strengthen the claim that the benchmark spans the intended capability spectrum.
- [Table 2] Table 2 (or the equivalent main-results table) reports aggregate scores; adding per-environment and per-subset breakdowns for the top three models in the same table would make the high-level vs. low-level contrast immediately visible without requiring the reader to consult the appendix.
- [§4.2] The success metric for low-level manipulation tasks is defined in terms of atomic-action completion; a one-sentence clarification of whether partial credit or strict binary success is used would remove ambiguity when comparing across models.
Simulated Author's Rebuttal
We thank the referee for their positive review, accurate summary of EmbodiedBench, and recommendation to accept the manuscript. We appreciate the recognition that the benchmark provides the first large-scale empirical map of MLLM limitations in embodied settings and that the public release of code, dataset, and evaluation harness supports reproducibility.
Circularity Check
No significant circularity: direct empirical benchmark results
full rationale
The paper introduces EmbodiedBench with 1,128 tasks across four simulated environments and six capability subsets, then reports direct performance measurements for 24 MLLMs. The central claim (e.g., GPT-4o averaging 28.9% with stronger high-level than low-level results) follows immediately from running the models on the defined task set. No equations, fitted parameters, predictions derived from inputs, or self-citation chains appear in the load-bearing steps; the work is a straightforward empirical evaluation whose results are not equivalent to its inputs by construction.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Performance on the selected simulated tasks and environments serves as a valid proxy for real-world embodied agent capabilities
Forward citations
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Reference graph
Works this paper leans on
-
[1]
Put washed lettuce in the refrigerator
Curran Associates Inc. ISBN 9781713829546. Calli, B., Singh, A., Walsman, A., Srinivasa, S., Abbeel, P., and Dollar, A. M. The ycb object and model set: Towards common benchmarks for manipulation research. In 2015 international conference on advanced robotics (ICAR), pp. 510–517. IEEE, 2015. Chang, M., Chhablani, G., Clegg, A., Cote, M. D., Desai, R., Hla...
-
[3]
**Visibility**: Always locate a visible object by the ’find’ action before interacting with it
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[4]
Avoid performing actions that do not meet the defined validity criteria
**Action Guidelines**: Make sure to match the action name and its corresponding action id in the output. Avoid performing actions that do not meet the defined validity criteria. For instance, if you want to put an object in a receptacle, use ’put down’ rather than ’drop’ actions
-
[6]
You can explore these instances if you do not find the desired object in the current receptacle
**Multiple Instances**: There may be multiple instances of the same object, distinguished by an index following their names, e.g., Cabinet 2, Cabinet 3. You can explore these instances if you do not find the desired object in the current receptacle
-
[7]
**Reflection on History and Feedback**: Use interaction history and feedback from the environment to refine and improve your current plan. If the last action is invalid, reflect on the reason, such as not adhering to action rules or missing preliminary actions, and adjust your plan accordingly. {ACTION HISTORY & ENVIRONMENT FEEDBACK (if available)} ## Now...
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[8]
Each plan should include no more than 20 actions
**Output Plan**: Avoid generating empty plan. Each plan should include no more than 20 actions
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[9]
**Visibility**: If an object is not currently visible, use the ”Navigation” action to locate it or its receptacle before attempting other operations
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[10]
Avoid performing actions that do not meet the defined validity criteria
**Action Validity**: Make sure to match the action name and its corresponding action id in the output. Avoid performing actions that do not meet the defined validity criteria
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[11]
Try to modify the action sequence because previous actions do not lead to success
**Prevent Repeating Action Sequences**: Do not repeatedly execute the same action or sequence of actions. Try to modify the action sequence because previous actions do not lead to success
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[12]
You can explore these instances if you do not find the desired object in the current receptacle
**Multiple Instances**: There may be multiple instances of the same object, distinguished by an index following their names, e.g., cabinet 2, cabinet 3. You can explore these instances if you do not find the desired object in the current receptacle
-
[13]
**Reflection on History and Feedback**: Use interaction history and feedback from the environment to refine and enhance your current strategies and actions. If the last action is invalid, reflect on the reason, such as not adhering to action rules or missing preliminary actions, and adjust your plan accordingly. {ACTION HISTORY & ENVIRONMENT FEEDBACK (if ...
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[18]
try to be as close as possible
*** Do not complete the task too early until you can not move any closer to the object, i.e. try to be as close as possible. {ICL EXAMPLES} ## Now the human instruction is: {TASK INSTRUCTION}. To achieve the task, 1. Reason about the current visual state and your final goal, and 2. Reflect on the effect of previous actions. 3. Summarize how you learned fr...
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[19]
on the front left side, a few steps from the current standing point)
Locate the Target Object Type: Clearly describe the spatial location of the target object from the ob- servation image (i.e. on the front left side, a few steps from the current standing point)
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[20]
When planning for movement, reason based on target object’s location and obstacles around you
Navigate by *** Using Move forward and Move right/left as the main strategy ***, since any point can be reached through a combination of those. When planning for movement, reason based on target object’s location and obstacles around you
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[21]
Focus on the primary goal: Only address invalid action when it blocks you from moving closer in the direction to target object. In other words, do not overly focus on correcting invalid actions when direct movement toward the target object can still bring you closer
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[22]
If so, plan nothing but ONE ROTATION at a step until that object appears in your view
*** Use Rotation Sparingly ***, only when you lose track of the target object and it’s not in your view. If so, plan nothing but ONE ROTATION at a step until that object appears in your view. After the target object appears, start navigation and avoid using rotation until you lose sight of the target again
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[23]
*** Do not complete task too early until you can not move any closer to the object, i.e. try to be as close as possible. {ICL EXAMPLES} ## Now the human instruction is: {TASK INSTRUCTION}. {ACTION HISTORY & ENVIRONMENT FEEDBACK (if available)} To achieve the task, 1. Reason about the current visual state and your final goal, and 2. Reflect on the effect o...
work page 2023
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[24]
There are two copper-colored pots visible on the stovetop
find a Knife3.pick up the Knife4.find a Tomato5.slice the Tomato6.find a Sink 7.put down the object in hand8.find a Tomato9.pick up the Tomato10.find a Microwave11.open the Microwave12.put down the object in hand 13.close the Microwave14.turn on the Microwave15.turn off the Microwave16.open the Microwave17.find a Tomato18.pick up the Tomato 19.close the M...
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[25]
navigate to the left counter in the kitchen2. pick up the spoon3. navigate to the table 24. place at the table 25. navigate to the TV stand6. pick up the sponge
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[26]
navigate to the left counter in the kitchen8. pick up the sponge9. navigate to the table 210. place at the table 211. navigate to the TV stand12. pick up the cleanser
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[27]
navigate to the left counter in the kitchen 15. navigate to the table 114. pick up the cleanser16. place at the table 1 Planning Step 1 (env step 1 –env step 6): Visual state description: The image shows a kitchen with wooden flooring, a refrigerator, a sink, and several cabinets. There are no visible objects such as a spoon, sponge, or cleanser in the im...
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[28]
Rotate to the left by 90 degrees18
Rotate to the right by 90 degrees17. Rotate to the left by 90 degrees18. Rotate to the left by 90 degrees 1.Move forward for 0.25 InvalidInvalid InvalidInvalidInvalidInvalid Figure 24. Reasoning Error Example in EB-Navigation: the agent recognized it was blocked by the countertop but failed to attempt navigating around it. 56
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