Models & learning
Embodied reasoning
Embodied reasoning is reasoning grounded in an agent's body, sensors, tools, and physical environment so that it can plan, select, or revise real-world actions. The term is broad and not standardised; in robotics it can include spatial understanding, task decomposition, grasp or trajectory reasoning, success detection, and replanning.
Also known as: embodied reasoning model
Updated
A broad capability rather than one algorithm
Embodied reasoning describes reasoning that must remain connected to a physical agent and situation. A robot may need to identify reachable objects, interpret spatial relationships, choose a grasp point, divide an instruction into steps, or decide whether an attempted step succeeded. The relevant body, camera views, tools, and safety constraints change what counts as a sensible answer.
The label does not identify one standard architecture or training method. A system may reason in language, images, coordinates, code, latent representations, or a combination. It may produce a plan for another policy to execute or interleave reasoning with its own actions.
Where it sits in a robot stack
The Gemini Robotics technical report separates an embodied-reasoning model from a vision-language-action model. The reasoning model handles capabilities such as spatial and temporal understanding, object and grasp prediction, and multi-view geometry; the VLA converts visual and language context into robot actions.
Gemini Robotics-ER 1.6 extends that high-level role to task planning, success detection, instrument reading, and calls to action models or other tools. Other robot systems can divide these responsibilities differently. “Embodied reasoning” should therefore be read as a capability claim that needs a stated interface and evaluation, not as a fixed model class.
Reasoning does not guarantee execution
A model can identify the right object and plan the right sequence while the robot still fails because of latency, balance, contact, calibration, or an unsuitable action representation. Spatial answers can also be wrong or become stale after the environment changes.
Useful evaluation separates reasoning quality from physical completion. It should test whether the model understands constraints, detects failures, and revises a plan, while also measuring whether the downstream policy and controllers execute that plan safely on the target robot.
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Related terms
Models & learning
Embodied AI
Embodied AI is artificial intelligence that perceives and acts through a body in an environment, so its actions change the observations available to it next. The body may be a physical robot or, in common research usage, an agent situated in a persistent simulated world. Embodied AI emphasises the coupled loop between morphology, perception, action, learning, and the environment.
Models & learning
Vision-language-action model
A vision-language-action model is a robot policy that conditions on visual observations and natural-language instructions to predict executable actions. It adapts a pretrained vision-language model, or a similar multimodal backbone, using robot trajectories so that semantic knowledge is connected to an embodiment’s action space. The action representation may use discrete tokens or continuous values.
Models & learning
Long-horizon task
A long-horizon task is a temporally extended robot task whose success depends on maintaining reliable behaviour across many actions, phases, or dependent subtasks. The term has no universal step-count threshold: it usually signals sequential dependencies, accumulating execution error, delayed outcomes, changing object state, or information that must be remembered beyond the current observation.
Models & learning
Policy
A policy is the decision rule that maps a robot’s current observations or estimated state, and sometimes a task instruction, to an action or probability distribution over actions. It can be hand-designed or learned from demonstrations, rewards or both. In humanoid robotics, its outputs may be joint targets, torques, end-effector changes or higher-level skills.