Humanoid Robotics: AI and Control Systems in 2026
Updated: July 29, 2026 (7531-08-07 in the Bulgarian calendar)
The hardware covered in our Hardware and Actuation piece only moves usefully once something decides what to do with it. That's the job of the software stack in this article: a model that turns a camera feed and a spoken instruction into coordinated motor commands, and the training pipeline that teaches it to do that reliably.
Vision-Language-Action Models: One Model, Not a Pipeline
Older robot software split perception, planning, and control into separate stages, each hand-tuned and each a place for errors to compound. The architecture that has taken over in 2026 is the vision-language-action (VLA) model: a single model that takes in a camera feed and a natural-language instruction and outputs action directly, as a sequence of motor commands, rather than as an intermediate plan a separate controller has to interpret. That lets a robot accept a task the way you'd brief a new hire — "pick up the box on the left and put it on the conveyor" — instead of requiring the task be hand-coded.
NVIDIA's Isaac GR00T platform is the most visible open reference point: GR00T 1.7 is a VLA model pretrained on roughly 32,000 hours of real human demonstration footage plus 8,000 hours of simulation, released as part of an end-to-end pipeline covering simulation, teleoperation-based data collection, training, and real-world deployment. Google DeepMind's parallel effort, Gemini Robotics, has followed a similar path — Gemini Robotics-ER 1.6, released in April 2026, added stronger spatial reasoning and an "agentic vision" capability aimed at high-precision industrial tasks, and is offered as an API so hardware makers can use it as a shared "brain" rather than building their own.
Build vs. Buy: Proprietary Models vs. a Shared Brain
2026 has split the industry into two camps. Companies that control their own robot end to end tend to keep the model in-house and tune it tightly to their own hardware: Figure AI ended its partnership with OpenAI in February 2025 and built Helix, its own in-house VLA model, which now runs Figure 03 in commercial deployment — including 40 units billing roughly $25 per robot-operating-hour at a BMW plant in Spartanburg as of January 2026. Tesla takes a similar vertically integrated approach, adapting networks derived from its Full Self-Driving stack to run on its own AI5 chip.
The alternative is treating the AI as a shared platform other hardware makers plug into. Boston Dynamics and Google DeepMind announced a partnership at CES 2026 to bring Gemini Robotics models to the electric Atlas; DeepMind is running the same playbook with Apptronik's Apollo and with Agile Robots' "Agile ONE" humanoid. On the compute side, NVIDIA's Jetson Thor module — the kind of embedded board pictured here — is becoming a common answer to where these models actually run onboard, with 1X citing it (alongside Isaac Sim, Isaac Lab, and NVIDIA's Blackwell GPUs for training) as part of the stack behind NEO.
Where the Training Data Comes From
A VLA model is only as good as what it was trained on, and in 2026 that training data comes from three complementary sources rather than one. Simulation — NVIDIA Isaac Sim and Isaac Lab are the dominant tools — handles locomotion and whole-body balance, where generating millions of trial-and-error attempts on real hardware would be slow and destructive. Teleoperation handles dexterous manipulation, where a human operator's hand and arm movements are captured and mapped onto the robot: approaches range from full exoskeleton rigs (used by companies like AgiBot) to wearable capture systems matched to a specific robot's joints (Fourier's approach for its GR-1), to systems like the Nexus NX1 that track 72 degrees of freedom of body and hand motion at sub-millimeter precision. Figure AI runs its own proprietary teleoperation fleet to generate data directly from its BMW deployment. The third source, large-scale egocentric human video, supplies general scene understanding — what a kitchen or a warehouse aisle actually looks like — that's cheap to collect but too generic to teach precise manipulation on its own.
None of these three sources is sufficient alone, which is why the companies furthest along in 2026 are the ones that have invested in data infrastructure — teleoperation fleets, simulation pipelines, data-labeling — as seriously as they've invested in the robot itself.
Where This Is Heading
The near-term direction pointed to by current research is smaller, more efficient models that can run on-device rather than depending on a cloud round-trip for every action, alongside a slow move toward open-weight models that smaller hardware makers can adopt without a partnership deal. That would narrow the gap between companies like Figure and Tesla that can afford to build a model from scratch, and smaller humanoid makers that currently have little choice but to plug into a shared platform like Gemini Robotics or Isaac GR00T.
Image credits: NVIDIA Jetson Nano photo by SparkFun Electronics, CC BY 2.0. Robotic exoskeleton photo by Paulo O, CC BY 2.0. Both via Wikimedia Commons.