Vision-Language-Action (VLA) Models

VLA models unify perception, language, and control — enabling robots to interpret visual scenes, parse spoken or written commands, and perform appropriate actions, all in a single computational loop.

Unlike traditional pipelines that link vision, NLP, and policy as separate modules, VLA systems train with shared representations or fully end-to-end architectures. This allows robots to respond to complex prompts like “Pick up the red cup on the left and hand it to me,” with grounded, executable behavior.Examples include SayCan, VoxPoser, VIMA, and PerAct (when paired with language planners). Many build on foundation models like CLIP, GPT-4, or RT-2 to support generalization across tasks and environments. Some VLA systems now also fuse tactile data for richer physical grounding and manipulation.