ProGAL-VLA: Grounded Alignment through Prospective Reasoning in Vision-Language-Action Models
Vision language action (VLA) models enable generalist robotic agents but often exhibit language ignorance, relying on visual shortcuts and remaining insensitive to instruction changes. We present Prospective Grounding and Alignment VLA (ProGAL-VLA), which constructs a 3D entity-centric graph (GSM), uses a slow planner to produce symbolic sub-goals, and aligns them with grounded entities via a Grounding Alignment Contrastive (GAC) loss. All actions are conditioned on a verified goal embedding $g_t$, whose attention entropy provides an intrinsic ambiguity signal. On LIBERO-Plus, ProGAL-VLA increases robustness under robot perturbations from 30.3 to 71.5 percent, reduces language ignorance b...
| Authors | Nastaran Darabi, Amit Ranjan Trivedi |
|---|---|
| Primary category | cs.RO |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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