How Foundational Skills Influence VLM-based Embodied Agents:A Native Perspective

• URL: http://arxiv.org/abs/2602.20687v1
• Date: Tue, 24 Feb 2026 08:42:41 GMT
• Title: How Foundational Skills Influence VLM-based Embodied Agents:A Native Perspective
• Authors: Bo Peng, Pi Bu, Keyu Pan, Xinrun Xu, Yinxiu Zhao, Miao Chen, Yang Du, Lin Li, Jun Song, Tong Xu,
• Abstract summary: We present NativeEmbodied, a benchmark for VLM-driven embodied agents.<n>Built on diverse simulated scenes, NativeEmbodied includes three representative high-level tasks in complex scenarios to evaluate overall performance.<n>For more detailed analysis, we construct four types of low-level tasks, each targeting a fundamental embodied skill.
• Score: 18.773467537970753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in vision-language models (VLMs) have shown promise for human-level embodied intelligence. However, existing benchmarks for VLM-driven embodied agents often rely on high-level commands or discretized action spaces, which are non-native settings that differ markedly from real-world control. In addition, current benchmarks focus primarily on high-level tasks and lack joint evaluation and analysis at both low and high levels. To address these limitations, we present NativeEmbodied, a challenging benchmark for VLM-driven embodied agents that uses a unified, native low-level action space. Built on diverse simulated scenes, NativeEmbodied includes three representative high-level tasks in complex scenarios to evaluate overall performance. For more detailed analysis, we further decouple the skills required by complex tasks and construct four types of low-level tasks, each targeting a fundamental embodied skill. This joint evaluation across task and skill granularities enables fine-grained assessment of embodied agents. Experiments with state-of-the-art VLMs reveal clear deficiencies in several fundamental embodied skills, and further analysis shows that these bottlenecks significantly limit performance on high-level tasks. NativeEmbodied highlights key challenges for current VLM-driven embodied agents and provides insights to guide future research.

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