Static and Plugged: Make Embodied Evaluation Simple
This provides a scalable and simplified evaluation method for researchers in embodied AI, though it is incremental as it builds on existing static representation ideas.
The paper tackles the problem of costly and fragmented evaluation in embodied intelligence by introducing StaticEmbodiedBench, a plug-and-play benchmark using static scene representations, which covers 42 scenarios and 8 dimensions and evaluates 30 models to establish a unified leaderboard.
Embodied intelligence is advancing rapidly, driving the need for efficient evaluation. Current benchmarks typically rely on interactive simulated environments or real-world setups, which are costly, fragmented, and hard to scale. To address this, we introduce StaticEmbodiedBench, a plug-and-play benchmark that enables unified evaluation using static scene representations. Covering 42 diverse scenarios and 8 core dimensions, it supports scalable and comprehensive assessment through a simple interface. Furthermore, we evaluate 19 Vision-Language Models (VLMs) and 11 Vision-Language-Action models (VLAs), establishing the first unified static leaderboard for Embodied intelligence. Moreover, we release a subset of 200 samples from our benchmark to accelerate the development of embodied intelligence.