arXiv:2606.02735v1 Announce Type: cross Abstract: Generalization remains a central bottleneck for vision-language-action (VLA) models: under distractors, appearance shifts, and semantically similar tasks, the policy must often infer local execution details from coarse instructions while also deciding which parts of the image matter for control. We present S2 (See Less, Specify More), a framework for improving VLA generalization by training the executor under a cleaner interface. Specify More preserves the original instruction as a stable high-level goal while relabeling each trajectory into re
The continuous development of more generalized and robust AI models, particularly in the vision-language-action (VLA) domain, is a key focus of current AI research, addressing central limitations.
Improving VLA model generalization through cleaner interfaces and reduced visual evidence is crucial for deploying AI in complex, real-world scenarios and enabling more autonomous systems.
This framework suggests a shift towards more efficient and effective training methodologies for VLA models, potentially leading to faster development and more reliable deployment in varied environments.
- · AI developers
- · Robotics industry
- · Automation sector
- · Developers relying on brute-force data approaches for VLA models
- · Manual labor in complex environments
Increased reliability and adaptability of AI-powered robotic systems in unstructured environments.
Accelerated adoption of AI agents in tasks requiring nuanced perception and control, impacting various industries from manufacturing to healthcare.
Potential for significantly more autonomous and versatile AI agents reducing human intervention and oversight in complex operation.
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Read at arXiv cs.LG