Red-Teaming Vision-Language-Action Models via Quality Diversity Prompt Generation for Robust Robot Policies
Summary: arXiv:2603.12510v2 Announce Type: replace-cross
Abstract
Vision-Language-Action (VLA) models hold great promise for enabling general-purpose robotic systems capable of performing a variety of vision-language tasks. However, the effectiveness of robots utilizing VLA technology is often sensitive to the specific phrasing of language instructions, creating challenges in predicting failure scenarios. To enhance the resilience of VLA models against varied linguistic expressions, we introduce Q-DIG (Quality Diversity for Diverse Instruction Generation). This innovative approach performs red-teaming by systematically identifying a diverse array of natural language task descriptions that not only induce failures but also remain relevant to the tasks at hand.
Q-DIG Methodology
Q-DIG seamlessly integrates Quality Diversity (QD) techniques with Vision-Language Models (VLMs) to produce a wide range of adversarial instructions. These instructions are essential for uncovering significant vulnerabilities in the behavior of VLA systems. The core of Q-DIG’s methodology can be summarized in the following steps:
- Identification of Diverse Instructions: Q-DIG focuses on generating a variety of prompts that can lead to failure.
- Integration with Vision-Language Models: The generated prompts are used to test and evaluate the robustness of VLA models.
- Analysis of Failure Modes: The approach emphasizes the importance of discovering and understanding the nature of failures.
Results and Findings
Our extensive evaluations across multiple simulation benchmarks demonstrate that Q-DIG successfully identifies a broader range of meaningful failure modes compared to traditional baseline methods. Key findings from our research include:
- Fine-tuning VLA models on Q-DIG generated instructions significantly enhances task success rates.
- User studies reveal that Q-DIG prompts are perceived as more natural and human-like compared to those generated by baseline techniques.
- Real-world testing of Q-DIG prompts yielded results consistent with simulations, further validating the method’s effectiveness.
Conclusion
In summary, Q-DIG represents a groundbreaking approach for identifying vulnerabilities in Vision-Language-Action models while simultaneously improving their robustness. By leveraging Quality Diversity techniques to generate diverse and effective prompts, we pave the way for more resilient robotic systems capable of navigating a wide array of language instructions. The implications of our research extend beyond academic interest, offering practical solutions for real-world applications in robotics. For more information, visit our project website at qdigvla.github.io.