SHORT ANSWER
The action modality is the dividing line.
Both VLMs and VLAs can process images and language. A VLM may caption a scene, answer a question, identify an object or reason about spatial relationships. A VLA is trained so that the multimodal representation also conditions robot actions.
COMPARISON
Same multimodal foundation, different operational role.
Typical inputs
Images or video plus text.
Typical outputs
Text, labels, coordinates, embeddings or other semantic representations.
Training emphasis
Large-scale image-text and multimodal understanding objectives.
Typical inputs
Images, language and often robot proprioception or embodiment state.
Typical outputs
Action tokens, continuous action vectors or temporal action chunks.
Training emphasis
Robot trajectories paired with observations and task instructions, often combined with prior VLM pretraining.
ARCHITECTURE
Many VLAs contain a VLM rather than replacing it.
OpenVLA starts from a pretrained vision-language architecture and adapts it to predict robot actions. SmolVLA explicitly combines a VLM backbone with an action expert. GR00T similarly uses a VLM to encode visual and language context while a separate action path processes robot state and actions.
WHY VLM ALONE IS NOT ENOUGH
Knowing where the cup is does not define how this robot should move.
A multimodal model can identify a cup and understand the instruction “put it in the sink,” but robot execution still depends on reachability, kinematics, gripper state, camera geometry, action representation and the target control interface. VLA training binds semantic understanding to examples of embodied action.
WHY VLA IS NOT THE WHOLE ROBOT
Adding action output still does not eliminate control, safety or hardware interfaces.
A VLA may predict low-level actions, but physical deployment still has responsibilities outside the learned model: sensor timing, calibration, actuator constraints, controller stability, emergency behavior and application-specific safety. VLA and VLM describe model roles, not complete robot architectures.
OPENRGD VIEW
VLM and VLA can both be cognition providers; the downstream contract determines authority.
In the OpenRGD candidate architecture, a VLM might produce semantic interpretation or a high-level proposal, while a VLA may produce a more action-oriented intent. Neither label alone determines whether the component has direct hardware authority.