# Embodied Reasoning

GenEA places an agent's coarse body biomechanics and its physical interactions with nearby objects into a multilevel state space, then embeds that space in a generative model to yield a joint probability distribution over goals and embodied plans. Earlier accounts of how people reason about plans mostly abstracted agents as point-like entities in two-dimensional grid worlds, which cannot carry the body and terrain constraints of natural settings. Across block-manipulation and obstacle-course navigation experiments, simple transformations of that joint distribution reproduce human goal inferences, action inferences, one-hand versus two-hand strategy preferences and difficulty judgments, outperforming a task-and-motion-planning baseline, heuristic models and a frontier vision-language model.

GenEA places an agent's coarse body biomechanics and its physical interactions with nearby objects into a multilevel state space, then embeds that space in a generative model to yield a joint probability distribution over goals and embodied plans. Earlier accounts of how people reason about plans mostly abstracted agents as point-like entities in two-dimensional grid worlds, which cannot carry the body and terrain constraints of natural settings. Across block-manipulation and obstacle-course navigation experiments, simple transformations of that joint distribution reproduce human goal inferences, action inferences, one-hand versus two-hand strategy preferences and difficulty judgments, outperforming a task-and-motion-planning baseline, heuristic models and a frontier vision-language model.

Google DeepMind's Gemini Robotics ER 2, a high-level reasoning model for robots, is now in public preview on the Gemini platform, letting enterprises declare low-level controls such as vision-language-action models and navigation APIs as tools for the model to orchestrate. The model classifies task progress from continuous video and pinpoints critical event frames, with a stated 91.3% accuracy and 0.96-second mean absolute frame distance. It is described as working across embodiments from stationary arms to humanoids, with simulation, evaluation and deployment handled on Google Cloud.

Google DeepMind's Gemini Robotics ER 2, a high-level reasoning model for robots, is now in public preview on the Gemini platform, letting enterprises declare low-level controls such as vision-language-action models and navigation APIs as tools for the model to orchestrate. The model classifies task progress from continuous video and pinpoints critical event frames, with a stated 91.3% accuracy and 0.96-second mean absolute frame distance. It is described as working across embodiments from stationary arms to humanoids, with simulation, evaluation and deployment handled on Google Cloud.

Gemini 4 Argon puts physical understanding, code generation and visual-semantic reasoning into one model so robot tasks can be practiced in simulation first. On the Embodied Reasoning (ERQA) benchmark, which tests physical and spatial problems in 3D environments, it scores 87% using pure reasoning, above other frontier models. It can also reconstruct calibrated simulation twins from robot camera feeds, egocentric human video or hand-drawn sketches, and generate closed-loop control and training data from them.

Gemini 4 Argon puts physical understanding, code generation and visual-semantic reasoning into one model so robot tasks can be practiced in simulation first. On the Embodied Reasoning (ERQA) benchmark, which tests physical and spatial problems in 3D environments, it scores 87% using pure reasoning, above other frontier models. It can also reconstruct calibrated simulation twins from robot camera feeds, egocentric human video or hand-drawn sketches, and generate closed-loop control and training data from them.