Google DeepMind launches Gemini Robotics 2 for humanoid control

Unlike the previous generation of Gemini Robotics, which primarily controlled a robot's upper body, the new VLA model can direct an entire humanoid from top to bottom ^[3]. Gemini Robotics ER 2 sits above it as the high-level agent: it streams video, audio, and text to plan multi-step tasks that can run for several minutes, then hands off the actual motor execution to lower-level VLA models ^[2].

The efficiency play is Gemini Robotics On-Device 2, which runs locally on the robot itself and can adapt to a completely new robot body using just a few hours of data, fewer than 200 demonstrations ^[1]. That flexibility shows up in the demos: the same stack drives Apptronik's Apollo through its five-fingered, 22-joint hand well enough to tie knots and seal a ziplock bag, then runs a simpler two-fingered gripper on a different platform without a rebuild ^[3]. DeepMind lists Boston Dynamics and Agile Robots among the hardware partners already plugged into the stack ^[4], with Franka also named as a partner platform ^[2], positioning Gemini Robotics as a single model stack designed to span humanoids, industrial arms, and mobile robots rather than being tied to one proprietary robot.

The headline whole-body upgrade is real, but the fine-motor numbers tell a more modest story: Gemini Robotics 2 hits 92% success unscrewing a light bulb, yet drops to 44% tying a trash bag and 40% sealing a ziplock bag ^[3]. DeepMind's own robotics director, Kanishka Rao, frames that gap as the field's central unsolved problem rather than a rounding error: robots still perform well only in scenarios they have already seen, and struggle to generalize to anything unfamiliar ^[3].

That caveat lines up with how the demos landed outside Google's own channels. A high-engagement Reddit discussion voiced general skepticism about real-world speed and reliability alongside its enthusiasm for the release. A separate, much lower-engagement community comment claimed a rival robotics company had already shown multi-robot coordination roughly a year earlier. That claim rests on a single comment, not a widely echoed critique, and is not enough on its own to treat as settled pushback on Gemini Robotics 2's novelty. DeepMind's own YouTube explainer clips for the release reinforce the official framing of steady, incremental capability gains, while the more skeptical read plays out in the comment threads underneath community posts. The debate captures the tension running through this launch: an unmistakable jump in what the model can attempt, alongside reliability numbers that still fall well short of unsupervised, real-world deployment.

Much of what makes Gemini Robotics 2 possible is not just DeepMind's modeling work but a dedicated supply of training data: Apptronik's expanded 'Robot Park' facility in Austin runs Apollo 2 humanoids through teleoperation and autonomous-execution sessions that feed straight back into training and refining the Gemini Robotics models, with Mercedes-Benz and GXO named as customers tied to that effort ^[5].

That data pipeline is opening just as the hardware landscape it depends on gets more complicated in the US. The Trump administration and FCC banned new imports of foreign-made, largely Chinese, humanoid and quadruped robots on national-security grounds, warning the devices could be remotely controlled or used for surveillance or cyberattacks ^[6]. China accounts for roughly 85% of the global humanoid robot market, so DeepMind's strategy of running one model stack across many robot form factors now runs up against a shrinking set of non-Chinese bodies it is actually permitted to run on inside the US ^[6].

As Gemini Robotics ER 2 takes on more agentic responsibility, planning multi-step tasks and coordinating several robots at once, DeepMind is pairing that autonomy with a new safety benchmark, ASIMOV-Agentic, built to test whether the reasoning model refuses unsafe commands, flags impossible tasks, and asks a human for help rather than improvising ^[7]. The underlying safety paper argues that an agent operating a physical body needs guardrails beyond a single model: refusing out-of-constraint tasks, triggering interventions on hardware faults, and shielding the lower-level VLA model from instructions it was never trained to handle ^[7].

DeepMind also describes ER 2 as its safest release yet in a more literal sense: it is better at detecting when a person is close to the robot and halting motion until the area clears, only resuming once the space is empty again ^[3]. Packaging that behavior alongside the capability upgrades suggests DeepMind sees safety certification, not just raw dexterity, as the gating factor for letting these models operate near people without supervision.