Embodied Knowledge Understanding Benchmarks

• The benchmark establishes structured protocols to evaluate AI abilities in perception, action, and interactive reasoning.
• It encompasses diverse modalities such as vision, language, 3D data, and multi-sensory inputs to measure both atomic and composite tasks.
• It employs detailed evaluation metrics to analyze performance in spatial, temporal, and planning dimensions.

Embodied knowledge understanding benchmarks are structured evaluation frameworks designed to rigorously test an AI system's ability to perceive, reason about, and interact with the physical world in ways analogous to humans and animals. These benchmarks span diverse modalities—including vision, language, action, and, in advanced cases, multimodal, multisensory, or multi-agent interactions—and serve to quantify the atomic skills and higher-level cognitive processes that underpin embodied intelligence. The current generation of embodied benchmarks systematically probes capabilities related to perception, spatial and temporal reasoning, physical dynamics, planning, multimodal cue integration, tool use, exploration, and coordination, using a combination of realistic scenarios, carefully annotated data, and multi-dimensional evaluation metrics.

1. Taxonomy of Embodied Knowledge Benchmarks

Embodied knowledge understanding benchmarks can be broadly categorized along several axes: environment complexity, sensory modality coverage, and the granularity of tasks measured.

a) Environment and Scenario Types

• Indoor and Object-centric: Many foundational benchmarks focus on indoor scenes or object manipulation (e.g., YouRefIt (Chen et al., 2021), BEAR (Qi et al., 9 Oct 2025), ShapeLLM/3D MM-Vet (Qi et al., 27 Feb 2024), CleanUpBench (Li et al., 7 Aug 2025)). Tasks include reference grounding (identifying, pointing, or bounding-boxing objects), spatial reasoning, part recognition, and manipulation planning.
• Urban/Open-world Spaces: Several recent efforts (e.g., EmbodiedCity (Gao et al., 12 Oct 2024), UrbanVideo-Bench (Zhao et al., 8 Mar 2025)) extend evaluation to urban 3D environments, demanding navigation, visual-language grounded QA, and complex scene understanding during agent motion.
• Egocentric vs. Third-person Views: Datasets such as AlanaVLM/EVUD (Suglia et al., 19 Jun 2024), EOC-Bench (Yuan et al., 5 Jun 2025), ECBench (Dang et al., 9 Jan 2025), VidEgoThink (Cheng et al., 15 Oct 2024), and EgoExoBench (He et al., 24 Jul 2025) capture both egocentric (first-person) and exocentric (third-person) visual experiences, enabling evaluation of cross-perspective understanding and temporal memory.

b) Modalities and Senses

Benchmarks vary in their emphasis on input/output modalities, including:

• Vision-Language: Most standard embodied benchmarks use RGB (sometimes RGB-D) image/video and naturalistic language as the primary modalities.
• 3D and Point Cloud: Advanced formats integrate explicit 3D data (ShapeLLM (Qi et al., 27 Feb 2024), CleanUpBench (Li et al., 7 Aug 2025)).
• Multi-sensory (beyond vision): At the frontier, benchmarks employ tasks based on the psychology of perception, covering tactile, auditory, olfactory, gustatory, and interoceptive senses (e.g., (Yang et al., 19 Oct 2025)).

c) Cognitive and Physical Capabilities

Tasks are organized either atomically or compositionally into domains such as:

• Low-level Perceptual Grounding: Pointing, bounding box localization, trajectory tracking (BEAR (Qi et al., 9 Oct 2025), YouRefIt (Chen et al., 2021)).
• Mid-level Reasoning: Spatial and temporal relationship reasoning (EmbSpatial-Bench (Du et al., 9 Jun 2024), MFE-ETP (zhang et al., 6 Jul 2024)), trajectory and causal reasoning.
• High-level Planning and Action: Multistage goal decomposition, next-action prediction, affordance reasoning, error/failure diagnosis (RoboBench (Luo et al., 20 Oct 2025), Omniear (Wang et al., 7 Aug 2025)).
• Tool Use and Multi-agent Coordination: Reasoning about when to acquire tools or collaborate with other agents (OmniEAR (Wang et al., 7 Aug 2025)).
• Dynamic Embodiment and Memory: Tracking objects and scene changes over time, predicting future states from ongoing interaction (EOC-Bench (Yuan et al., 5 Jun 2025), EmRACE-3K (Lin et al., 14 Jul 2025)).

2. Methodological Approaches and Data Annotation

Effective embodied knowledge benchmarks are constructed through careful scene generation, data capture, and annotation pipelines:

• Crowdsourcing and Realistic Capture: Datasets like YouRefIt (Chen et al., 2021) and EVUD (Suglia et al., 19 Jun 2024) utilize crowdworkers to collect egocentric or naturalistic indoor/outdoor scene videos, ensuring diversity and ecological validity.
• Scene Annotation: Fine-grained human-in-the-loop annotation yields spatiotemporal bounding boxes, gesture labels, canonical frames, and language parses. Example: YouRefIt provides temporal segmentation, hand keypoints (from OpenPose), bounding boxes, and language decomposition.
• Automated Generation with Human Curation: Recent large-scale benchmarks (e.g., VidEgoThink (Cheng et al., 15 Oct 2024), UrbanVideo-Bench (Zhao et al., 8 Mar 2025)) leverage LLMs (e.g., GPT-4o) to bootstrap question–answer pairs, with subsequent filtering for diversity and elimination of trivial or commonsense-answerable items.
• Task Granularity: Hybrid formats combine closed-form (multiple choice, true/false, classification) and open-ended questions, and tasks may require explicit visual reference prompts (point, bounding box, mask).

3. Evaluation Metrics, Protocols, and Benchmarking Paradigms

Benchmarking frameworks utilize domain-specific and generalized evaluation protocols that capture the multi-faceted nature of embodied cognition.

a) Perceptual and Spatial Evaluation

• Intersection over Union (IoU): For localization, e.g., YouRefIt uses IoU at various thresholds (0.25/0.5/0.75) for referred object detection.
• Coverage Ratio, Sweep Redundancy: CleanUpBench (Li et al., 7 Aug 2025) evaluates exploration efficiency in robotic sweeping/grasping via CR = A₍covered₎/A₍total₎ and spatial redundancy metrics.

b) Temporal and Sequential Evaluation

• Multi-Scale Temporal Accuracy (MSTA): EOC-Bench (Yuan et al., 5 Jun 2025) utilizes $$\mathrm{MSTA} = \frac{1}{|C|} \sum_{\alpha \in C} \mathbb{1}(\Delta T \leq \alpha T_{gt})$$ across scalable error thresholds to assess the temporal precision of predictions.

c) Reasoning and Planning

• Long-horizon Planning Score (RoboBench (Luo et al., 20 Oct 2025)):

$$\mathrm{LongHorizon} = \frac{\mathrm{NodeCorrectness} + \mathrm{TaskCompletion}}{20}$$

where $\mathrm{NodeCorrectness}$ and $\mathrm{TaskCompletion}$ are computed over predicted versus ground-truth plan nodes and critical object state milestones.

d) Holistic and Consensus Evaluation

• Score Aggregation with ECEval (ECBench (Dang et al., 9 Jan 2025)):

$S = \frac{n}{5}, \quad n \in \{0, 1, 2, 3, 4, 5\}$

providing multi-level scoring for open-ended answers against full and partial references.

• Exploration-Answer Consistency (EXPRESS-Bench (Jiang et al., 14 Mar 2025)):

$$C = \frac{1}{N} \sum_{i=1}^N \frac{\sigma_i \cdot \delta_i}{5} \times 100\%$$

fuses correctness with visual evidence of agent exploration.

4. Capabilities Probed and Model Performance

These benchmarks reveal rich insights and persistent limitations in state-of-the-art systems:

• Multimodal Cue Coordination: Explicitly evaluated via strong multimodal fusion, as in YouRefIt (Chen et al., 2021), which demonstrates that incorporating body gesture and pointing saliency cues provides significant gains over language- or vision-only models.
• Scaling Laws: The VEC benchmark (Li et al., 2023) demonstrates that visual concepts (e.g., color, material) exhibit scaling benefits in very large LMs (e.g., OPT-175B achieves 85% on material), but embodied concepts like mass and temperature do not follow these trends and require visual supervision.
• Spatial Reasoning and Navigation: EmbSpatial-Bench (Du et al., 9 Jun 2024) and EmbodiedCity (Gao et al., 12 Oct 2024) show that even advanced LVLMs (e.g., GPT-4V) underperform on egocentric spatial relation tasks, achieving <50% accuracy versus >90% for humans.
• Temporal Dynamics and Memory: Datasets like EOC-Bench (Yuan et al., 5 Jun 2025) and ECBench (Dang et al., 9 Jan 2025) expose deficits in persistent object tracking, retrospection, and dynamic relationship modeling.
• Planning and Task Execution: RoboBench (Luo et al., 20 Oct 2025), MFE-ETP (zhang et al., 6 Jul 2024), and EmRACE-3K (Lin et al., 14 Jul 2025) reveal that long-horizon planning, multi-stage goal decomposition, and accurate task state estimation are major bottlenecks, with success rates for top models often below 20% on open-domain multi-step interactive tasks.
• Tool Use and Collaboration: OmniEAR (Wang et al., 7 Aug 2025) demonstrates that models exhibit steep drops in success when tasks require autonomous tool acquisition or implicit multi-agent coordination—fine-tuning yields large gains in single-agent tasks (up to 76.3%), but only marginal improvements (to ~5.5%) in collaborative, constraint-driven reasoning.

5. Integration of Enhanced Agents and Tool Augmentation

Addressing benchmarked deficiencies, several works propose agent architectures that incorporate modular external vision/extraction tools, memory, and reasoning prompts:

• BEAR-Agent (Qi et al., 9 Oct 2025): This modular agent integrates pretrained models (e.g., GroundingDINO, DepthAnything) and category-specific analytical routines, which, when layered atop base MLLMs, deliver notable absolute improvements (e.g., +9.12% on BEAR, +20.17% on simulation tasks).
• PhysAgent (Chow et al., 27 Jan 2025): Fuses outputs from specialized perceptual modules (depth, segmentation, object detection) and a physical knowledge memory with chain-of-thought prompting, yielding an 18.4% gain for GPT-4o on PhysBench.
• Distillation and Alignment: Knowledge transfer strategies—e.g., Neuron Selectivity Transfer using Maximum Mean Discrepancy (MMD) losses (Li et al., 2023)—allow distilled vision-derived embodied knowledge to augment otherwise text-only models with human-level improvements at far lower parameter costs.

6. Implications, Open Challenges, and Future Directions

Embodied knowledge understanding benchmarks serve dual roles: (1) diagnosing current system abilities and deficits and (2) indicating productive lines of architectural and methodological refinement.

• Limits of Current Models: Model deficiencies include poor spatial localization in egocentric settings (EmbSpatial-Bench (Du et al., 9 Jun 2024), EOC-Bench (Yuan et al., 5 Jun 2025)), weak tool acquisition and collaboration strategies (OmniEAR (Wang et al., 7 Aug 2025)), and brittle long-horizon planning (RoboBench (Luo et al., 20 Oct 2025)).
• Visual Grounding Paradox: Studies such as (Yang et al., 19 Oct 2025) reveal that, counter-intuitively, current vision–LLMs do not reliably outperform text-only models on embodied knowledge understanding benchmarks, especially for visual and spatial tasks. Their performance is often hampered by embedding biases drawn from word frequency and form, indicating inadequate grounding in real perceptual experience.
• Importance of Multi-sensory and Active Learning: Robust embodied knowledge likely requires richer, multi-sensory input streams, dynamic (interactive or self-supervised) data acquisition, and learning regimes that go beyond static, pre-aligned image–text corpora. There is a growing impetus to incorporate tactile, auditory, and proprioceptive feedback (Yang et al., 19 Oct 2025, Li et al., 2023).
• Advanced Evaluation and Benchmark Design: Future benchmarks are expected to extend temporal horizons (longer videos, streaming tasks), increase behavioral and environmental diversity, feature richer agent–environment and inter-agent interactions, and provide granular scoring (e.g., ECEval (Dang et al., 9 Jan 2025)) to drive real-world robustness in deployed agents.

7. Broader Impact and Standardization Efforts

Embodied knowledge understanding benchmarks form the backbone for evaluating, comparing, and iteratively improving multimodal foundation models, vision–language agents, and robotics systems as embodied brains. By articulating a spectrum of atomic and composite abilities grounded in perception, action, and reasoning, these benchmarks:

• Encourage the development of compositional skills (e.g., BEAR’s atomic capabilities (Qi et al., 9 Oct 2025), EmRACE-3K’s spatial-semantic reasoning (Lin et al., 14 Jul 2025)).
• Highlight the importance of grounding, contextual memory, and robust simulation-to-reality transfer (UrbanVideo-Bench (Zhao et al., 8 Mar 2025), CleanUpBench (Li et al., 7 Aug 2025), ShapeLLM (Qi et al., 27 Feb 2024)).
• Provide standardization through shared code, data (e.g., (Dang et al., 9 Jan 2025, Du et al., 9 Jun 2024)), and APIs, fostering community-driven improvement and cross-institutional comparability.

These initiatives collectively propel the field toward the development of foundational models and agent architectures that can operate, reason, and adapt within the heterogeneity and unpredictability of the physical world.