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This paper proposes ManipBench, a novel benchmark for evaluating low-level reasoning in robotic manipulation. While Vision-Language Models (VLMs) are primarily used as high-level planners in robotic manipulation, research on their low-level reasoning (determining precise robot actions) has also been conducted recently. ManipBench evaluates the low-level reasoning capabilities of VLMs in robotic manipulation across various aspects, including object-to-object interaction and manipulation of deformable objects. Thirty-three representative VLMs from ten model families are extensively tested on the benchmark, analyzing model performance differences and correlations with real-world manipulation tasks. This analysis reveals a significant gap between current models and human-level understanding.
Takeaways, Limitations
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Takeaways:
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We present a new benchmark (ManipBench) that comprehensively evaluates the low-level robotic manipulation reasoning capabilities of VLMs.
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We compare and analyze the performance of various VLMs and present correlations with actual tasks.
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It clearly shows the difference between the current technological level of VLMs and human level.
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Limitations:
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Since ManipBench is still an early stage benchmark, more models and tasks will need to be added in the future.
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Further review and improvement of the benchmark design and evaluation metrics may be required.
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Further evaluation is needed for more complex robotic manipulation tasks that are beyond the scope of current benchmarks.
