arXiv:2606.14375v1 Announce Type: cross Abstract: Vision-language-action (VLA) models are powerful action generators for robot manipulation, but they are typically executed with fixed inference and replanning schedules. This rigidity ignores the uneven difficulty of robot control: contact-rich or uncertain states may need more computation and fresher feedback, while easier states can often be handled with fewer inference steps and longer open-loop execution. We propose Elastic Queries Reinforcement Learning (EQRL), a framework that makes each VLA policy query elastic. A lightweight latent-sche
The increasing sophistication of VLA models and the push for more efficient, real-world robotic applications necessitate dynamic resource allocation rather than rigid execution schedules.
This development addresses a critical efficiency bottleneck in robotics by enabling VLA models to adapt their computational intensity based on task complexity, leading to more robust and economical robot deployment strategies.
Robot control systems can now dynamically adjust inference and replanning frequencies, making VLA models more power-efficient, reliable in varied environments, and economically viable for widespread adoption.
- · Robotics companies
- · AI research labs
- · Logistics and manufacturing sectors
- · Hardware developers for VLA models
- · Inefficient robot architectures
- · Companies reliant on fixed-schedule inference
- · Energy-intensive VLA model applications
Robots will operate longer and more reliably in real-world, unstructured environments due to adaptive computational loads.
The reduced operational cost and increased robustness could accelerate the commercialization and deployment of general-purpose humanoid robots and automation across industries.
More efficient VLA models could reduce the energy footprint of robotic operations, partially mitigating the 'energy-bottleneck' narrative as AI demands grow.
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Read at arXiv cs.AI