WHAT VLA MEANS
Vision and language become inputs; action becomes an output modality.
Google DeepMind describes Gemini Robotics as a VLA family that turns visual information and instructions into robot actions, while NVIDIA describes GR00T N models as VLA models for humanoid robot learning and control. The exact action representation, temporal horizon and control surface differ across systems, so “VLA” names a model family rather than one universal interface.
MODEL LAYERS
Reasoning and action generation do not have to be the same model.
Perception
Images, video and other sensor-derived context describe the current environment.
Language
Instructions and task context specify goals, constraints or interaction.
Embodied reasoning
A higher-level model can understand physical space and plan multi-step work.
VLA policy
The action model converts multimodal context into robot actions or motor-control outputs.
On-device execution
Some VLA designs are optimized to run locally where latency or connectivity matter.
Adaptation
Modern systems increasingly target multiple robot embodiments rather than one fixed body.
MULTI-EMBODIMENT
Portability increases the importance of explicit body context.
Recent robotics models explicitly target different robot shapes and hardware. DeepMind’s current On-Device 2 materials, for example, describe adaptation to new robot embodiments and sensors. That makes a hidden assumption dangerous: the same high-level task does not imply the same reachable actions, sensing, payload or safety envelope on every body.
OPENRGD CANDIDATE BOUNDARY
A cognition provider may be a VLA without becoming the hardware authority.
In the current OpenRGD candidate Cognition Contract, a cognition provider may be an LLM, VLM, VLA, world model or deterministic planner. The proposed boundary is deliberately model-agnostic: a provider emits explicit action semantics and evidence references rather than gaining direct actuator authority by default.
IMPORTANT LIMIT
This is an OpenRGD candidate architecture, not a universal rule for all VLA systems.
Some production or research VLA systems output motor-control actions directly. OpenRGD’s candidate architecture introduces an explicit semantic and safety boundary because it is optimizing for inspectability, heterogeneous embodiments and downstream accountability. Those contracts remain candidate and non-normative until promoted through OpenRGD governance.