How Foundational Skills Influence VLM-based Embodied Agents:A Native Perspective
arXiv:2602.20687v1 Announce Type: new 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 evaluat
arXiv:2602.20687v1 Announce Type: new 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.
Executive Summary
This article introduces NativeEmbodied, a novel benchmark for vision-language models (VLMs) driven embodied agents. NativeEmbodied addresses limitations in existing benchmarks by utilizing a unified, native low-level action space, simulating diverse scenarios, and evaluating performance across task and skill granularities. Experimental results reveal deficiencies in fundamental embodied skills, highlighting key challenges for current VLM-driven agents. The study provides valuable insights to guide future research in embodied intelligence, emphasizing the need for more nuanced evaluation and analysis. By shedding light on the limitations of existing VLMs, the authors contribute to the development of more sophisticated and human-like embodied agents.
Key Points
- ▸ NativeEmbodied is a novel benchmark for VLM-driven embodied agents, addressing limitations in existing benchmarks.
- ▸ The benchmark utilizes a unified, native low-level action space and simulates diverse scenarios.
- ▸ Experimental results reveal deficiencies in fundamental embodied skills, highlighting key challenges for current VLM-driven agents.
Merits
Strength in Methodology
The authors' approach to constructing a benchmark that evaluates embodied agents across task and skill granularities is a significant improvement over existing methods, providing a more comprehensive understanding of embodied intelligence.
Insight into Embodied Skills
The study provides valuable insights into the fundamental embodied skills required for human-like embodied intelligence, shedding light on the limitations of current VLMs and guiding future research in this area.
Demerits
Scope of Evaluation
The benchmark's focus on low-level action spaces and simulated scenarios may limit its generalizability to real-world settings, where embodied agents may face varying and unpredictable conditions.
Data and Computational Resources
The high computational requirements and substantial data needed for training and evaluating VLMs on the NativeEmbodied benchmark may pose significant challenges for researchers and practitioners with limited resources.
Expert Commentary
The introduction of NativeEmbodied represents a significant step forward in evaluating and analyzing embodied agents, providing a more nuanced understanding of the fundamental skills required for human-like embodied intelligence. By shedding light on the limitations of current VLMs, the authors contribute to the development of more sophisticated and human-like embodied agents, which can have far-reaching implications for various applications and industries. Additionally, the study's findings highlight the need for further research in embodied cognition and human-robot interaction, emphasizing the importance of addressing the challenges and limitations of current VLMs.
Recommendations
- ✓ Future research should focus on developing more sophisticated and human-like embodied agents, addressing the challenges and limitations of current VLMs.
- ✓ The development of more efficient and scalable methods for training and evaluating VLMs on the NativeEmbodied benchmark is necessary to facilitate widespread adoption and application.