qhjqhj00 eSpatial-eval
EmbodiedVSR: Dynamic Scene Graph-Guided Chain-of-Thought Reasoning for Visual Spatial Tasks — Yi Zhang et al. (2025) (arXiv:2503.11089, 2025)
What this evaluates
Probes multimodal models' ability to perform complex, long-horizon spatial reasoning and physical consistency checks in dynamic, embodied scenarios. It evaluates object attribute recognition, relational understanding, and robotic manipulation planning across static images and real-world assembly tasks.
Datasets
- eSpatial-Benchmark — total 1005; splits: test (1005)
Metrics
accuracy(primary) — range: percent- Percentage of correctly predicted answers or attributes out of the total number of instances. Calculated as (correct predictions / total instances) * 100.
success_rate— range: percent- Percentage of real-world robotic assembly tasks completed successfully out of the total number of randomized tests.
Input / output format
Input: RGB or RGB-D images of spatial scenes or LEGO structures, accompanied by textual prompts or questions regarding spatial relationships, object attributes, or manipulation instructions.
Output: Textual chain-of-thought reasoning followed by a final answer (e.g., spatial relationship label, attribute value, or structured command for robot assembly).
Scoring recipe
def compute_accuracy(predictions, gold_labels):
correct = sum(1 for p, g in zip(predictions, gold_labels) if p.strip().lower() == g.strip().lower())
return (correct / len(gold_labels)) * 100
def compute_success_rate(successes, total_tests):
return (successes / total_tests) * 100
Common pitfalls
- Models often struggle with depth perception and precise object localization when relying solely on prompting, leading to VLM confusion where the model misinterprets its own outputs.
- Evaluating real-world robotic assembly requires accounting for occlusion challenges and physical constraints not fully captured in static image benchmarks.
- Accuracy metrics may mask failures in multi-step chain-of-thought reasoning if only the final answer is checked without verifying intermediate reasoning steps.
Evidence (verbatim from paper)
In 20 real-world randomized block assembly tests, EmbodiedVSR achieved a 100% accuracy in describing the block assembly, while the robot’s operational success rate was 80%.
Citation
@misc{zhang2025embodiedvsr,
title={EmbodiedVSR: Dynamic Scene Graph-Guided Chain-of-Thought Reasoning for Visual Spatial Tasks},
author={Yi Zhang et al. (2025)},
year={2025},
note={arXiv:2503.11089}
}
- arXiv: 2503.11089