Run-time Observation Interventions Make Vision-Language-Action Models More Visually Robust (2410.01971v1)

Abstract: Vision-language-action (VLA) models trained on large-scale internet data and robot demonstrations have the potential to serve as generalist robot policies. However, despite their large-scale training, VLAs are often brittle to task-irrelevant visual details such as distractor objects or background colors. We introduce Bring Your Own VLA (BYOVLA): a run-time intervention scheme that (1) dynamically identifies regions of the input image that the model is sensitive to, and (2) minimally alters task-irrelevant regions to reduce the model's sensitivity using automated image editing tools. Our approach is compatible with any off the shelf VLA without model fine-tuning or access to the model's weights. Hardware experiments on language-instructed manipulation tasks demonstrate that BYOVLA enables state-of-the-art VLA models to nearly retain their nominal performance in the presence of distractor objects and backgrounds, which otherwise degrade task success rates by up to 40%. Website with additional information, videos, and code: https://aasherh.github.io/byovla/ .

• The paper demonstrates BYOVLA improves VLA performance by applying runtime interventions that mitigate task-irrelevant visual distractions without model tuning.
• It employs a three-step process involving localization of distractions, sensitivity probing, and image transformation to enhance model robustness.
• Evaluations on Octo-Base and OpenVLA reveal a 20–40% improvement in task success rates in visually cluttered environments.

Enhancing Robustness in Vision-Language-Action Models with BYOVLA

This paper introduces BYOVLA (Bring Your Own Vision-Language-Action Model), a novel approach aimed at enhancing the robustness of Vision-Language-Action (VLA) models against task-irrelevant visual distractions. VLA models, while beneficial for performing diverse tasks by coupling vision and language to actions, often exhibit susceptibility to slight variations in the task environment, undermining their utility as generalist policies. The proposed BYOVLA framework offers a lightweight, model-agnostic intervention that operates at runtime to improve performance without necessitating model tuning or access to model weights.

Methodology and Implementation

The methodology involves a three-step process:

1. Localize Task-Irrelevant Objects: Utilizing vision-LLMs (VLMs), the framework identifies regions irrelevant to the task.
2. Apply Visual Sensitivity Probe: The system assesses which irrelevant regions the VLA is sensitive to by perturbing different segments of the visual input and measuring changes in the model’s output.
3. Transform the Image: For regions deemed sensitive, image editing tools are used to minimally alter the areas, such as inpainting or color alteration, thereby reducing their impact on the model’s performance.

This intervention is remarkably compatible with any off-the-shelf VLA model. BYOVLA was tested using Octo-Base and OpenVLA on manipulation tasks in a simulated kitchen environment. The results exhibited an improvement of 20–40% in task success rates in scenarios with visual distractions.

Key Contributions and Results

The paper’s main contribution is demonstrating that BYOVLA enhances visual robustness in VLA models by selectively modifying the input images in real-time without altering the model architecture or weights. The paper also highlights the broad applicability of BYOVLA, showing positive results across different VLA platforms.

• Octo-Base Evaluation: A notable 40% improvement in success rate was achieved by applying BYOVLA to environments cluttered with visual distractions, such as irrelevant objects and background alterations.
• OpenVLA Evaluation: Despite being highly trained on large-scale datasets, OpenVLA saw a reduction in task success when encountering distractions, yet BYOVLA restored much of the performance lost to these distractors.

Implications and Future Directions

The implications of this work are both practical and theoretical, presenting a framework that others can integrate with existing VLA models to improve real-world applicability. This approach can be particularly valuable in robotics, where environments are dynamic and unpredictable.

Future Work: Several extensions of this work are conceivable, such as exploring BYOVLA’s effectiveness in dynamic environments, enhancing the distinction between object and background distractions, or improving integration with segmentation and inpainting tools. Additionally, expanding its application to a broader range of tasks and models could further validate its utility.

The paper proposes that run-time interventions provide a promising pathway to enhance the baseline capabilities of VLA models by adapting to environment-specific challenges without further training. This could have significant ramifications in fields reliant on robotic automation and interaction under varying conditions. The seamless incorporation of such interventions could become a standard practice in deploying VLA systems in complex and dynamic real-world settings.