Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation
arXiv:2606.04046v1 Announce Type: cross Abstract: In embodied vision-language decision making tasks such as robotic manipulation and navigation, Vision-Language and Vision-Language-Action Models (VLMs & VLAs) are powerful tools with different benefits: VLMs are better at long-term planning, while VLAs are better at reactive control. However, their performance is limited by the same perceptual bottleneck: visual hallucinations arise due to the models' inability to distinguish task-relevant objects from distractors. In principle, accurate identification and focus on critical objects while filter
This research addresses a critical limitation in current Vision-Language Models (VLMs) and Vision-Language-Action Models (VLAs), which are rapidly advancing but still face perceptual bottlenecks in real-world embodied AI tasks.
Overcoming the 'perceptual bottleneck' through focus plan generation is crucial for scaling embodied AI applications like robotic manipulation and autonomous navigation, making them more robust and reliable.
The proposed 'focus plan generation' could enable VLMs and VLAs to more accurately identify and filter task-relevant objects, significantly improving their decision-making and reducing errors in complex environments.
- · Robotics companies
- · Embodied AI developers
- · Logistics and manufacturing sectors
- · Companies relying on less sophisticated vision systems
- · Systems highly susceptible to visual hallucinations
Embodied AI systems become more effective and less prone to errors in real-world applications.
Accelerated adoption of intelligent robots and autonomous systems in various industries due to improved reliability.
Increased integration of AI into complex physical tasks, potentially leading to new forms of automation and human-robot collaboration.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG