Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics (2411.13587v3)

Abstract: Recently in robotics, Vision-Language-Action (VLA) models have emerged as a transformative approach, enabling robots to execute complex tasks by integrating visual and linguistic inputs within an end-to-end learning framework. While VLA models offer significant capabilities, they also introduce new attack surfaces, making them vulnerable to adversarial attacks. With these vulnerabilities largely unexplored, this paper systematically quantifies the robustness of VLA-based robotic systems. Recognizing the unique demands of robotic execution, our attack objectives target the inherent spatial and functional characteristics of robotic systems. In particular, we introduce two untargeted attack objectives that leverage spatial foundations to destabilize robotic actions, and a targeted attack objective that manipulates the robotic trajectory. Additionally, we design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments. Our evaluation reveals a marked degradation in task success rates, with up to a 100\% reduction across a suite of simulated robotic tasks, highlighting critical security gaps in current VLA architectures. By unveiling these vulnerabilities and proposing actionable evaluation metrics, we advance both the understanding and enhancement of safety for VLA-based robotic systems, underscoring the necessity for continuously developing robust defense strategies prior to physical-world deployments.

• The paper introduces novel untargeted (UADA, UPA) and targeted (TMA) attacks to expose critical vulnerabilities in VLA models.
• Empirical evaluations across simulation and real-world platforms demonstrate up to 100% failure rates in task execution.
• Findings underscore the urgent need for robust defense mechanisms and revised training strategies to secure robotic deployments.

Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics

The paper "Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics" addresses critical security aspects of Vision-Language-Action (VLA) models, which have emerged as pivotal in advancing robotics through their ability to synthesize visual and linguistic inputs for executing tasks. This paper systematically investigates the robustness of VLA models against adversarial attacks, emphasizing the necessity of understanding these vulnerabilities before deploying such models in real-world scenarios.

Core Contributions and Methodological Insights

The paper introduces specific objectives for attacking VLA models by leveraging both untargeted and targeted adversarial strategies. Untargeted Action Discrepancy Attack (UADA) and Untargeted Position-aware Attack (UPA) focus on maximizing the deviation of robot actions from desired trajectories. UADA emphasizes maximizing normalized action discrepancies by considering the action range, while UPA concentrates on disrupting spatial trajectories by introducing directional perturbations.

The paper also proposes a Targeted Manipulation Attack (TMA) aimed at forcing the model to predict actions leading to specific erroneous executions. This analysis is framed within the context of simulating both digital and realistic environments, exploring adversarial patches that serve as effective vectors in compromising task success.

Results and Implications

Empirical evaluation conducted on simulation platforms such as BridgeData V2 and LIBERO, as well as real-world robotics settings, showcases that both UADA and UPA significantly degrade task success rates. Notably, the results demonstrate up to 100% failure rates in simulated environments and substantial disruptive capabilities in physical settings. These findings underscore the criticality of addressing security gaps inherent in VLA models to ensure safety in practical applications.

The implications of this work extend into both theoretical and applied robotics domains. Practically, the paper's techniques underscore the risk and potential failure points in deploying AI-driven robotic systems under adversarial conditions. Theoretically, it lays the groundwork for future advancements in designing robust VLA models by developing defense mechanisms that mitigate such vulnerabilities.

Future Directions

The research advocates for paradigm shifts in training strategies, suggesting multi-robot scenarios and new logical structures for identifying and nullifying adversarial perturbations—actions critical to enhancing the generalization and resilience of VLA models. Such directions are vital in bringing about advancements not only in improving VLA models' security but also in harmonizing their integration into diverse environmental contexts.

In conclusion, this paper's exploratory approach presents practical insights and methodologies necessary for advancing the security of VLA models in robotics, highlighting an area of pressing concern as these systems transition towards broader real-world applicability.