Over the past 18 months, the divide between language model capabilities and real-world robotic deployment has narrowed dramatically. A new breed of foundation models—designed for physical action rather than text generation—is now operating on actual hardware in factories, warehouses, and research labs. These systems include deployed robot policies, private-preview vision-language-action (VLA) models, open-weight research models, and world models for scaling robot training data. Some are undergoing evaluation or deployment with industrial partners; others remain primarily research or developer-facing. Here is a look at the ten most significant models in 2026.
NVIDIA Isaac GR00T N-Series (N1.5 / N1.6 / N1.7)
NVIDIA released the original GR00T N1 at GTC in March 2025 as the world's first open, fully customizable foundation model for generalized humanoid reasoning and skills. The N-series has advanced rapidly since. GR00T N1.5, announced at COMPUTEX in May 2025, introduced a frozen VLM, Eagle 2.5 grounding improvements, a FLARE training objective enabling learning from human egocentric videos, and the GR00T-Dreams blueprint—which reduced synthetic data generation from months to about 36 hours.
GR00T N1.6 followed on December 15, 2025, featuring a new internal NVIDIA Cosmos-2B VLM backbone supporting flexible resolution, a 2× larger DiT (32 layers versus 16 in N1.5), state-relative action chunks for smoother motion, and several thousand additional hours of teleoperation data from bimanual YAM arms, AGIBot Genie-1, and Unitree G1. It was validated on real bimanual and locomanipulation tasks across those embodiments.
The most recent release, GR00T N1.7 Early Access (April 17, 2026), is a 3B-parameter open, commercially licensed VLA built on a Cosmos-Reason2-2B backbone with a 32-layer DiT for low-level motor control—an Action Cascade dual-system architecture. Its central advance is EgoScale: pretraining on 20,854 hours of human egocentric video spanning 20+ task categories, significantly scaling beyond the robot teleoperation hours used in prior versions. NVIDIA identified what it describes as the first-ever scaling law for robot dexterity—going from 1,000 to 20,000 hours of human egocentric data more than doubles average task completion. N1.7 Early Access is available on HuggingFace and GitHub under Apache 2.0 licensing, with full production support tied to the general availability release. Early adopters across the GR00T N-series include AeiRobot, Foxlink, NEURA Robotics, and Lightwheel.
Google DeepMind Gemini Robotics 1.5
Gemini Robotics is an advanced VLA model built on Gemini 2.0, adding physical actions as a new output modality for directly controlling robots. It launched in March 2025 alongside Gemini Robotics-ER (Embodied Reasoning). The September 2025 update, Gemini Robotics 1.5, introduced agentic capabilities—turning visual information and instructions into motor commands while making the model's reasoning process transparent, helping robots assess and complete complex multi-step tasks more legibly.
Access remains available to selected partners including Agile Robots, Agility Robotics, Boston Dynamics, and Enchanted Tools, and is not publicly available. The broader family continues to evolve: Gemini Robotics-ER 1.6, released April 14, 2026, enhances spatial reasoning and multi-view understanding—including a new instrument reading capability developed in collaboration with Boston Dynamics for reading complex gauges and sight glasses. Gemini Robotics-ER 1.6 is available to developers via the Gemini API and Google AI Studio.
Physical Intelligence π0 / π0.5 / π0.7
π0 proposes a flow matching architecture built on top of a pre-trained vision-language model to inherit internet-scale semantic knowledge, trained across multiple dexterous robot platforms including single-arm robots, dual-arm robots, and mobile manipulators. Physical Intelligence open-sourced π0 in February 2025.
π0.5 was published on April 22, 2025, with openpi weights released later in 2025. Rather than targeting improved dexterity, its focus is open-world generalization: the model uses co-training across heterogeneous tasks, multiple robots, high-level semantic prediction, and web data to clean unfamiliar kitchens and bedrooms not seen in training. A subsequent version applied the RECAP (RL with Experience & Corrections via Advantage-conditioned Policies) approach—training by demonstration, coaching through corrections, and improving from autonomous experience—which Physical Intelligence reported doubled throughput on tasks such as inserting a filter into an espresso machine, folding previously unseen laundry, and assembling a cardboard box.
The most recent public research release is π0.7, published April 16, 2026. It is a research-stage system focused on compositional generalization: combining learned skills from different contexts to solve tasks the model was never explicitly trained on. Physical Intelligence describes it as a steerable model with emergent capabilities—an early but meaningful step toward a general-purpose robot brain. The paper uses careful hedging language throughout, and no commercial deployment timeline has been stated.
Figure AI Helix
Released February 20, 2025, Helix is the first VLA to output high-rate, continuous control of the entire humanoid upper body, including wrists, torso, head, and individual fingers. It uses a dual-system design: System 2 is a 7B-parameter internet-pretrained VLM operating at 7–9 Hz for scene understanding and language comprehension; System 1 is an 80M-parameter cross-attention encoder-decoder transformer running at 200 Hz, translating S2's semantic representations into precise continuous robot actions. The model was...