New models and versions, open weights, and changes in capability and pricing, with their evaluations.
Gemini 4 Argon taught a robot to play cornhole just by watching a video. It mapped the physics, coded its own simulation, and taught @Apptronik's Apollo humanoid to sink 7 throws in a row.
Why it matters: The demonstration connects video observation, physics modeling, and a model-written simulation in a concrete example of teaching a humanoid robot a physical task.
Google DeepMind introduces Gemini 4 Argon, a general-purpose physical reasoning model for robot control, simulation reconstruction, physical understanding, and self-improvement. Google DeepMind reports an 87% ERQA score using pure reasoning and a simulation-generated training example that raises real-world robot success from 56% to 75% while reducing completion time from 35s to 24s. Argon is available today to Google AI Ultra subscribers through Google AI Studio and Google Antigravity, and through the paid Gemini API tier using gemini-4-argon in the Interactions API.
Why it matters: The simulation-to-hardware example provides a concrete basis for assessing how generated training episodes can improve robot task success and completion speed within a reported setup.
Google DeepMind has released Gemini Robotics ER 2, positioned as the robot's high-level brain: it converses with people, understands the physical world, and plans multi-step tasks before handing execution to a lower-level VLA model. It can also call tools such as Google Search natively.
Why it matters: The official release gives concrete figures for ER 2's progress classification and temporal localization, along with access details, helping readers assess its limits on long tasks.
Google DeepMind has released Gemini Robotics 2 with three models: Gemini Robotics 2, a vision-language-action model for translating visual and language input into motion; Gemini Robotics ER 2 for embodied reasoning; and Gemini Robotics On-Device 2, which runs locally on devices.
Why it matters: The article explains the three models' roles and access points, helping readers assess pathways to full-body robot control and multi-robot collaboration.
Mistral AI has released Robostral Navigate, its first embodied-navigation model. The 8B model uses a single standard RGB camera, with no LiDAR or depth sensor, and achieves a 76.6% success rate on R2R-CE validation unseen—9.7 percentage points above the best single-camera approach and 4.5 points above the best systems using depth or multiple cameras.
Why it matters: The source reports navigation success using a monocular RGB camera and describes the training approach, giving a view of how sensor requirements for embodied navigation are changing.
Google DeepMind has released Gemini Robotics-ER 1.6, a major upgrade to its reasoning-first robotics model. It improves spatial reasoning and multi-view understanding and adds the ability to read complex instruments such as pressure gauges and level meters.
Why it matters: The source details changes in spatial reasoning, multi-view understanding, and instrument reading, along with access information, giving a view of progress in high-level robot reasoning.
