Gemini 4 Argon for Robotics

OCT. 13, 2026

Gemini 4 Argon marks a step change in physical understanding, code generation, and visual-semantic reasoning—unlocking new workflows across the robotics stack and accelerating progress toward physical AGI.

You can see this leap in capability on the Embodied Reasoning (ERQA) benchmark, which tests a model's ability to understand and solve physical, spatial problems in 3D environments. Using pure reasoning, Argon scores 87%, outperforming every other frontier model.

As a general-purpose physical reasoning model, Argon can be applied across the entire robotics lifecycle. In this post, we share representative prompts for Argon across four key areas:

• Robot control → Mobile manipulation with MuJoCo practice, zero-shot robot control, and closed-loop steering of video world models.
• Real2Sim → Reconstructing physically calibrated simulation twins from robot camera feeds, egocentric human video, or hand-drawn paper sketches.
• Physical understanding → Agentic multimodal hindsight labeling including detailed understanding of dexterity, dense value functions and progress estimation.
• Research and self-improvement → Action improvement via simulation and iterative self-improvement on hardware.

Unlocking robot control with Argon

1. Spot mobile manipulation: Putting in the trash bin with MuJoCo practice

Argon controls a Boston Dynamics Spot® quadruped (12-DoF legs + 6-DoF arm) to learn a new skill: stepping on a foot-pedal trash bin to throw away soda cans. Argon creates an articulated MuJoCo digital twin of a foot-pedal trash bin to practice its moves before stepping on the physical pedal and dropping soda cans into the open bin.

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Spot Mobile Manipulation — Real-to-Sim-to-Real foot-pedal trash bin disposal: Spot picking up the soda can, checking its wrist camera, practicing 3-leg tripod balance in MuJoCo, stepping on the foot pedal, verifying hand centering in the open bin, and dropping the can inside.

Prompt:

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