CLAW: A Vision-Language-Action Framework for Weight-Aware Robotic Grasping

Understand

Vision-language-action (VLA) models have recently emerged as a promising paradigm for robotic control, enabling end-to-end policies that ground natural language instructions into visuomotor actions.

• However, current VLAs often struggle to satisfy precise task constraints, such as stopping based on numeric thresholds, since their observation-to-action mappings are implicitly shaped by training data and lack explicit mechanisms for condition monitoring.
• In this work, we propose CLAW (CLIP-Language-Action for Weight), a framework that decouples condition evaluation from action generation.
• CLAW leverages a fine-tuned CLIP model as a lightweight prompt generator, which continuously monitors the digital readout of a scale and produces discrete directives based on task-specific weight thresholds.