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arxiv: 2605.06234 · v2 · pith:HHHFOMJRnew · submitted 2026-05-07 · 💻 cs.RO · cs.HC

RobotEQ: Transitioning from Passive Intelligence to Active Intelligence in Embodied AI

Kuofei Fang , Xinyi Che , Haomin Ouyang , Shufan Zhang , Xuehao Wang , Qi Liu , Liyi Liu , Chenqi Zhang
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Wenxi Cai Wenyu Dai Jinyang Wu Fan Zhang Haoyu Chen Bin He Zheng Lian
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Pith reviewed 2026-05-08 09:07 UTC · model grok-4.3

classification 💻 cs.RO cs.HC
keywords embodied AIactive intelligencesocial normsbenchmarkrobot actionsegocentric imagesspatial grounding
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The pith

The first benchmark for active intelligence shows embodied AI models still cannot reliably follow social norms without explicit instructions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper establishes RobotEQ as the first benchmark to measure active intelligence in embodied AI systems. Active intelligence means a robot can judge which actions are permissible according to social norms even without direct user commands, in contrast to passive intelligence that follows explicit instructions. The authors create RobotEQ-Data with 1,900 egocentric images across 10 categories, annotated with 5,353 action judgment questions and 1,286 spatial grounding questions, then introduce RobotEQ-Bench to test state-of-the-art models. Results indicate current models perform poorly, especially on spatial tasks, though retrieval-augmented generation with external social norm knowledge improves outcomes. The benchmark supports shifting robotics from command-driven manipulation toward proactive social compliance.

Core claim

RobotEQ is introduced as the first benchmark for active intelligence, which enables robots to judge permissible actions based on social norms in embodied settings absent explicit instructions. The accompanying RobotEQ-Data contains 1,900 egocentric images across 10 categories and 56 subcategories, annotated with 5,353 action judgment questions and 1,286 spatial grounding questions. RobotEQ-Bench applies this to assess state-of-the-art models, finding they fall short particularly in spatial grounding while benefiting from retrieval-augmented generation with social norm knowledge.

What carries the argument

The RobotEQ benchmark, built on the RobotEQ-Data dataset of manually annotated egocentric images and questions about permissible robot actions and spatial grounding, together with the RobotEQ-Bench evaluation protocol.

If this is right

  • Existing models cannot yet achieve reliable active intelligence in embodied scenarios.
  • Performance is weakest on spatial grounding tasks that require understanding physical constraints in context.
  • Incorporating external social norm knowledge via retrieval techniques generally improves adherence to permissible actions.
  • This benchmark can facilitate the transition of robotics from user-guided passive manipulation to active social compliance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Robots with effective active intelligence could manage unexpected situations in homes or public spaces with less human oversight.
  • Expanding the benchmark to include dynamic video sequences or multi-turn interactions would better test real-time norm following.
  • The spatial grounding weakness points to a broader need for tighter coupling between visual perception and normative reasoning in embodied models.

Load-bearing premise

The manually annotated action judgments and spatial questions in RobotEQ-Data accurately and comprehensively represent real-world social norms and permissible robot behaviors across diverse embodied scenarios.

What would settle it

A physical robot running a model that scores high on RobotEQ-Bench is deployed in varied human environments and observed for the frequency of actions that violate social norms when no instructions are given.

Figures

Figures reproduced from arXiv: 2605.06234 by Bin He, Chenqi Zhang, Fan Zhang, Haomin Ouyang, Haoyu Chen, Jinyang Wu, Kuofei Fang, Liyi Liu, Qi Liu, Shufan Zhang, Wenxi Cai, Wenyu Dai, Xinyi Che, Xuehao Wang, Zheng Lian.

Figure 1
Figure 1. Figure 1: RobotEQ. This benchmark consists of multiple robot-view images covering typical embodied categories and subcategories. It provides two types of questions: action judgment and spatial grounding. For action judgment, both proper and improper actions are annotated; for spatial grounding, both appropriate and inappropriate regions or movement trajectories are labeled. whether robots can successfully complete t… view at source ↗
Figure 2
Figure 2. Figure 2: Data collection pipeline. 1) Scenario design. We define scenario categories and subcate￾gories, and then employ LLMs to generate diverse image descriptions. 2) Image generation. These descriptions serve as input for image generation. Since generated images may contain artifacts, we further refine them using image editing. 3) Action judgment. For each image, we compile a list of candidate actions and annota… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of RobotEQ-Data. (a) Key statistics of the benchmark. (b) Distribution of the ten scenario categories. (c) Distribution of the eight evaluation dimensions. which was subsequently calibrated by a domain expert to establish the final ground truth. Based on annotator accuracy, we selected the 7 highest-performing annotators to form the formal labeling team. This pilot phase ensured the reliability an… view at source ↗
Figure 4
Figure 4. Figure 4: Dimension-level action judgment performance. Radar charts compare representative models with human performance across the eight dimensions in RobotEQ-Bench. Qwen3-VL-8B Claude Sonnet 4.6 GUI-Actor-7B Gemma-3-12B Claude Opus 4.7 GPT-5.5 Nanonets-OCR2 GroundNext-7B Gemini 2.5 Pro InfiGUI-G1 InternVL3-8B LLaVA-OneVision GLM-4.1V-9B DeepSeek-VL2 45.0 47.5 50.0 52.5 55.0 57.5 60.0 62.5 Macro-F1 (%) Macro-F1 (%)… view at source ↗
Figure 5
Figure 5. Figure 5: Spatial grounding. Human performance is annotated alongside each subplot title. 5.3 Error Analysis To better understand model limitations, we examine representative GPT-5.5 [33] errors on action judgment and spatial grounding in view at source ↗
Figure 6
Figure 6. Figure 6: Representative error cases from GPT-5.5. We categorize failures into four types: Overly Aggressive, Overly Cautious, Lack of Social Experience, and Spatial Grounding Error. For CoT prompting, we guide the model to reason through a fixed sequence before making the final judgment: scene analysis, demand recognition, role reflection, and action assessment. This prompt encourages the model to consider both the… view at source ↗
Figure 7
Figure 7. Figure 7: presents the complete taxonomy. We briefly summarize the 10 major categories below view at source ↗
Figure 8
Figure 8. Figure 8: Prompt templates for scenario generation. Overview of the beam-phase and merge-phase prompts used in RobotEQ-Data, highlighting the input fields, generation constraints, deduplication rules, and expected output structure view at source ↗
Figure 9
Figure 9. Figure 9: Representative scenario examples. Five example scenarios illustrating how embodied agents must reason over nonverbal cues, spatial relations, and context-specific social norms in real￾world human environments. B Image Generation The scenarios produced by the beginning of the generation pipeline in Section 3.1 are textual descriptions. They specify the social context, the position of the agent, and the envi… view at source ↗
Figure 10
Figure 10. Figure 10: Scenario-to-image prompt synthesis. An example of how RobotEQ-Data converts a structured embodied social scenario into a visual prompt for image generation. The prompt preserves the social interaction conflict, specifies visual anchors and spatial relations, and produces a first￾person scene image for benchmark construction. Image Generation. The synthesized visual prompts are then used to generate candid… view at source ↗
Figure 11
Figure 11. Figure 11: Examples of image refinement. Representative raw and edited images from the automated refinement stage. The examples illustrate how the editing process improves visual grounding and scenario fidelity while preserving the intended embodied social context. Human Verification. After the automated revision stage, we aggregate the original image, the edited image, the corresponding scenario, and the scenario d… view at source ↗
Figure 12
Figure 12. Figure 12: Examples of the Label Studio annotation interface. The left panel shows the human verification stage where annotators compare original and edited scenario images, and the right panel shows the human annotation stage for action judgment and spatial grounding labelling. Additional cases are omitted for brevity. C Action Generation The action generation stage aims to construct, for each validated scenario, a… view at source ↗
Figure 13
Figure 13. Figure 13: Action generation prompt. Illustration of the prompt structure used to generate candidate action pools from a scenario image and its textual description. the benchmark, such as physically impossible actions, irrelevant actions, or actions that do not form a meaningful test of active intelligence. All annotations are collected through a Label Studio interface configured for this task. Pilot Study. To calib… view at source ↗
Figure 14
Figure 14. Figure 14: Action judgment evaluation example. The figure illustrates the input format used for action judgment in RobotEQ. Given a first-person scenario image, the model receives a role-specific question and a list of candidate actions, and must assign each action a binary label indicating whether it should or should not be performed. Please select the signal area in the diagram that you believe indicates a custome… view at source ↗
Figure 15
Figure 15. Figure 15: Comparison of sptaial grounding question generation pipelines. Representative examples comparing the two-stage and one-stage construction procedures for spatial grounding questions. The two-stage pipeline produces more precise and visually grounded spatial annotations, while the one-stage pipeline is more prone to misplaced, overly broad, or spatially incoherent annotations. produces generic questions or … view at source ↗
Figure 16
Figure 16. Figure 16: Spatial grounding evaluation example. The figure illustrates the input and output format for a spatially grounded multiple-choice question in RobotEQ-Data. Given an annotated robot-view scene image and a question, the model selects all applicable spatial regions and provides a brief rationale for its prediction. selected in Appendix D label spatial grounding questions through a Label Studio interface. We … view at source ↗
Figure 17
Figure 17. Figure 17: Chain-of-Thought prompt design for action judgment. The figure illustrates the CoT input view at source ↗
Figure 18
Figure 18. Figure 18: Example of a role-specific RAG knowledge base. The figure shows a representative view at source ↗
read the original abstract

Embodied AI is a prominent research topic in both academia and industry. Current research centers on completing tasks based on explicit user instructions. However, for robots to integrate into human society, they must understand which actions are permissible and which are prohibited, even without explicit commands. We refer to the user-guided AI as passive intelligence and the unguided AI as active intelligence. This paper introduces RobotEQ, the first benchmark for active intelligence, aiming to assess whether existing models can comprehend and adhere to social norms in embodied scenarios. First, we construct RobotEQ-Data, a dataset consisting of 1,894 egocentric images, spanning 10 representative embodied categories and 56 subcategories. Through extensive manual annotation, we provide 4,944 action judgment questions and 1,157 spatial grounding questions, specifying appropriate robot actions across diverse scenarios. Furthermore, we establish RobotEQ-Bench to evaluate the performance of state-of-the-art models on this task. Experimental results demonstrate that current models still fall short in achieving reliable active intelligence, particularly in spatial grounding. Meanwhile, leveraging RAG techniques to incorporate external social norm knowledge bases can generally enhance performance. This work can facilitate the transition of robotics from user-guided passive manipulation to active social compliance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces RobotEQ as the first benchmark for 'active intelligence' in embodied AI, defined as the ability of robots to comprehend and adhere to social norms without explicit user instructions (contrasted with 'passive intelligence' for user-guided tasks). It constructs RobotEQ-Data from 1,900 egocentric images across 10 categories and 56 subcategories, providing 5,353 manually annotated action judgment questions and 1,286 spatial grounding questions. RobotEQ-Bench evaluates state-of-the-art models, reporting underperformance (especially in spatial grounding) that can be mitigated by RAG with external social norm knowledge bases. The work positions this as facilitating a transition to active social compliance in robotics.

Significance. If the annotations reliably capture generalizable social norms across embodied scenarios, RobotEQ could provide a valuable standardized benchmark for evaluating and improving social awareness in embodied AI, addressing a gap beyond explicit task completion. The empirical findings on model limitations and RAG benefits offer concrete directions for future work. The introduction of the 'active intelligence' framing, while novel, would benefit from stronger ties to existing literature on ethical robotics and value alignment.

major comments (2)
  1. [RobotEQ-Data construction] §3 (RobotEQ-Data construction): The dataset relies on 'extensive manual annotation' to create the 5,353 action judgment and 1,286 spatial grounding questions, but reports no inter-annotator agreement metrics, annotator demographics, or external validation against established ethical corpora or incident databases. This is load-bearing for the central claim that RobotEQ measures active intelligence, as social norms are culturally variable and the benchmark's validity as a faithful proxy depends on annotation reliability and generalizability.
  2. [RobotEQ-Bench evaluation] §4 (RobotEQ-Bench evaluation): The results claim that current models 'fall short' in active intelligence and that RAG 'can generally enhance performance,' but provide no specific quantitative metrics (e.g., accuracy or F1 scores per category), baseline model details, or error analysis across the question sets. This limits verification of the underperformance extent and the improvement magnitude, weakening the empirical support for the benchmark's utility.
minor comments (1)
  1. [Abstract and Introduction] The abstract and introduction assert RobotEQ is the 'first benchmark' for active intelligence without citing or contrasting against prior datasets on social norms, ethical decision-making, or value alignment in robotics/AI.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below, indicating revisions where appropriate to strengthen the work.

read point-by-point responses

  1. Referee: The dataset relies on 'extensive manual annotation' to create the 5,353 action judgment and 1,286 spatial grounding questions, but reports no inter-annotator agreement metrics, annotator demographics, or external validation against established ethical corpora or incident databases. This is load-bearing for the central claim that RobotEQ measures active intelligence, as social norms are culturally variable and the benchmark's validity as a faithful proxy depends on annotation reliability and generalizability.

    Authors: We agree that inter-annotator agreement metrics are essential to demonstrate annotation reliability, given the cultural variability of social norms. In the revised manuscript, we will report Fleiss' kappa scores computed on a 10% re-annotated subset of the questions. We will also add a description of annotator demographics, noting that the team consisted of researchers with expertise in robotics and AI ethics. For external validation, we will expand the discussion to explicitly map our 10 categories and 56 subcategories to established social norm frameworks from ethical robotics literature (e.g., value alignment studies), while acknowledging this as an area for future work rather than claiming full external corpus validation. revision: yes

  2. Referee: The results claim that current models 'fall short' in active intelligence and that RAG 'can generally enhance performance,' but provide no specific quantitative metrics (e.g., accuracy or F1 scores per category), baseline model details, or error analysis across the question sets. This limits verification of the underperformance extent and the improvement magnitude, weakening the empirical support for the benchmark's utility.

    Authors: We will revise §4 to include a detailed results table reporting accuracy and F1 scores broken down by the 10 categories (and where feasible, subcategories) for both action judgment and spatial grounding tasks. We will explicitly list the evaluated models (including versions and prompting details) and add a dedicated error analysis subsection identifying common failure modes, such as spatial mis-grounding and norm misinterpretation. These changes will provide verifiable quantitative support for the reported underperformance and RAG benefits. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark construction with independent annotations

full rationale

The paper presents RobotEQ as an empirical benchmark for active intelligence via manual annotation of 1,900 images into 5,353 action judgments and 1,286 spatial questions across 10 categories. No mathematical derivations, equations, fitted parameters, or predictions appear in the provided text. The central claims rest on dataset construction and model evaluation, which do not reduce to self-citations, self-definitions, or inputs by construction. This is a standard benchmark-creation effort whose validity can be assessed externally against real-world norms or inter-annotator metrics, with no load-bearing step that collapses into its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The work rests on the assumption that social norms are annotatable and generalizable for robot actions. No free parameters are used as this is a benchmark paper rather than a fitted model. The invented distinction between passive and active intelligence structures the contribution but lacks external validation.

axioms (1)
  • domain assumption Social norms can be consistently defined and manually annotated as appropriate or inappropriate robot actions in embodied scenarios.
    The benchmark depends on 5,353 action judgment questions derived from manual annotation, assuming these reflect objective and representative norms.
invented entities (1)
  • active intelligence no independent evidence
    purpose: To label the capability of understanding permissible actions without explicit user commands, in contrast to passive intelligence.
    New terminology introduced in the abstract to frame the benchmark; no independent evidence or falsifiable prediction is provided beyond the definition.

pith-pipeline@v0.9.0 · 5573 in / 1310 out tokens · 83976 ms · 2026-05-08T09:07:25.499262+00:00 · methodology

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