Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation

Researchers introduce SceneDiver, a new method that improves Vision-Language Models and Vision-Language-Action Models by reducing visual hallucinations through progressive scene understanding and focus planning. The approach uses a coarse-to-fine strategy to help AI systems distinguish task-relevant objects from distractors, with applications in robotic manipulation and navigation tasks.

SceneDiver addresses a fundamental limitation in embodied AI systems: visual hallucinations that occur when vision-language models fail to distinguish critical objects from irrelevant background elements. This perceptual bottleneck has constrained the practical deployment of both VLMs and VLAs in real-world robotic applications where accurate scene understanding directly determines task success.

The research builds on growing recognition that effective AI decision-making in physical environments requires more sophisticated visual processing than current single-step attention mechanisms provide. Prior approaches attempted direct focus on essential objects, but this fails because meaningful focus requires deep contextual understanding. SceneDiver's innovation lies in its coarse-to-fine strategy: first building a holistic scene graph for broad comprehension, then iteratively decomposing tasks into simpler sub-problems through cycles of recognition, understanding, and analysis.

The practical impact extends across multiple embodied AI domains. In robotic manipulation, distinguishing the target object from similar distractors directly improves task completion rates. In navigation scenarios, understanding scene layout prevents misinterpretation of environmental features. The authors' lightweight adapter for distilling focus abilities into VLAs ensures the method remains computationally efficient for real-time robotic control, addressing a critical constraint in autonomous systems deployment.

Developers working with embodied AI systems should monitor this approach's validation results across standard benchmarks. The released code and data enable broader testing and potential integration into existing robotic platforms. Future work likely involves scaling these insights to more complex multi-object scenes and adapting the methodology for different robotic morphologies and task domains.

• →SceneDiver reduces visual hallucinations in vision-language models through progressive scene graph analysis rather than single-step focus mechanisms.
• →The method preserves computational efficiency for real-time robotic control while improving accuracy in both planning and reactive tasks.
• →A coarse-to-fine decomposition strategy enables better distinction between task-relevant and distractor objects in embodied AI scenarios.
• →The lightweight adapter design allows focus planning capabilities to be efficiently integrated into VLAs for practical deployment.
• →Released code and benchmarks enable broader adoption and validation across robotic manipulation and navigation applications.