Google DeepMind's Gemini Robotics 2 Can Control a Humanoid From Feet to Fingertips

Google DeepMind released Gemini Robotics 2 on 30 July 2026, a vision-language-action model that controls an entire humanoid robot: walking, balancing, crouching, and handling objects with five-fingered hands. Apptronik's Apollo 2 is the demonstration platform.

The previous generation drove a robot's upper body. This one runs legs, torso, arms and fingers under a single learned policy, which is what DeepMind means by whole-body control.

The strategic story is bigger than the demo reel. Google is not trying to win the humanoid hardware race. Its play is to be the intelligence layer inside robots that other companies build.

What Gemini Robotics 2 actually is

DeepMind shipped three models, and they do different jobs.

Gemini Robotics 2 is the vision-language-action model. It turns camera input and spoken instructions into motor commands, and it can drive full humanoids as well as two-armed robots using either multi-finger hands or conventional parallel grippers.

Gemini Robotics ER 2 is the embodied reasoning model, built on Gemini 3.5 Flash. DeepMind describes it as the robot's high-level brain. It holds a conversation, plans multi-step tasks, tracks its own progress, calls tools, and coordinates more than one robot on a shared job. It accepts interleaved text, images, video and audio with a 128k context window.

Gemini Robotics On-Device 2 runs locally, without a network connection. Built on Gemini Robotics 1.5 and Google's Gemma models, it adapts to an unfamiliar robot body in a few hours using fewer than 200 example demonstrations.

What the demonstrations show

The clearest example is mundane on purpose. Told to put the watering can into the green bin on the bottom shelf, Apollo 2 walks across the room, finds the can, picks it up, crouches, and places it where it was asked. Nobody scripted the sequence of movements.

Other demonstrations cover unscrewing a light bulb, tying a knot in a rubbish bag, sealing a ziplock bag, sweeping debris into a dustpan, and packing items tightly using grippers. Some run for several minutes. DeepMind also shows two robots working the same task, coordinated by the ER model.

The hardware on show includes Apollo 2 fitted with SharpaWave and Inspire hands, a Franka Duo with a Robotiq gripper, and smaller research platforms from Dexmate and Trossen plus the open-source SO101 arm.

The success rates Google published

DeepMind published task-level numbers, and they are candid about how uneven performance still is.

The light bulb pair is the tell. Taking a bulb out succeeds 92% of the time. Putting one back in succeeds 36% of the time. Removal tolerates rough alignment, insertion does not, and the space between those two numbers is roughly where robot dexterity sits today.

The other two models publish their own scorecards. ER 2 picks the right moment out of a video 91.3% of the time, with a mean error of 0.96 seconds, but it judges how far along a task is correctly only 57.4% of the time. On-Device 2 shows the sharpest generational jump: adapting to the open-source SO101 arm takes it from 6.7% to 53.3%, and on Dexmate hardware from 24.4% to 75.6%. The previous on-device model finished those same two adaptations at 6.7% and 33.3%.

Why this matters more than the demo

Humanoid hardware is fragmenting. Apptronik, Figure, Tesla, Unitree, Boston Dynamics, AgiBot and a dozen others are all building bodies. Foundation models are consolidating instead, because training them takes data and compute that most robot makers cannot fund alone.

The result is an Android-shaped possibility: many robot bodies, a small number of intelligence suppliers underneath them. Google already demonstrates Gemini across humanoids, two-armed systems, multi-finger hands and grippers, and it signed an AI partnership with Boston Dynamics in January 2026 covering the next-generation Atlas.

Read that as an interpretation of Google's multi-platform strategy rather than a proven commercial outcome. No licensing terms have been published and the whole-body model is not on sale.

The reality check

A demonstration is not a deployment. Several things are worth holding onto before treating this as solved.

• You cannot buy the thing in the videos. ER 2 is in Google AI Studio and in private preview on the Gemini Enterprise Agent Platform, but the whole-body model and the on-device model go only to early-access partners through a trusted tester programme.
• Three of the five hand tasks DeepMind published complete less than half the time.
• Speed is still a problem, and DeepMind says so.
• DeepMind also states that the on-device model struggles to generalise to unfamiliar tasks and to control robots with many degrees of freedom.
• Nothing here describes a real building. Uptime, intervention rate, safety incidents and cost per completed task are all absent.

On safety, DeepMind introduced ASIMOV-Agentic, a benchmark for agentic safety orchestration and uncertainty resolution, and showed the model detecting a nearby human and bringing the robot to a safe stop. That is real engineering, but a benchmark is not a certification or an insurer's sign-off.

What it means for humanoid manufacturers

Apptronik gains the most immediately. Apollo is the platform every clip is filmed on, and Apptronik runs Robot Park, a nearly 90,000 square foot facility in Austin that it opened on 30 June 2026, collecting the training data that feeds Gemini Robotics.

Boston Dynamics is the other named beneficiary. Its January 2026 partnership puts Gemini Robotics models into the new Atlas perception and reasoning stack, and DeepMind is one of only two customers taking 2026 Atlas units.

Figure and Tesla sit on the opposite side of the bet. Both build their own hardware and their own models. If Google's layer wins, vertical integration starts to look expensive. If it does not, they keep the whole margin. Our Figure 03 versus Apollo 2 comparison covers how differently those two software stacks are being built.

Smaller manufacturers get the most interesting option: license intelligence rather than fund a frontier model. None of that is real until Google publishes commercial terms.

What buyers should take from this

Gemini Robotics 2 is good evidence that general robot intelligence is improving faster than most operations teams expect. It is not evidence that you can buy a humanoid, install Gemini, and automate arbitrary work.

If you are evaluating a humanoid this year, the questions have not changed. Ask for task-level completion rates on your task, not on a benchmark. Ask for intervention rate per shift. Ask for safety documentation and the deployment support model. Ask for total installed cost rather than unit price. A 92% score on a light bulb tells you nothing about your production line.

For where the current machines actually stand, see our ranking of the best humanoid robots, and follow the humanoid robot news tracker for what lands next.