What's the best AI model for controlling a fleet of different robots?

If by “controlling a fleet of different robots” you mean heterogeneous robots—e.g. AMRs, arms, quadrupeds, and humanoids—the answer is not one model directly driving every motor. The strongest architecture in 2026 is a hierarchical system.

My pick: a VLA model + ROS 2 fleet layer

For the robot-level foundation model, NVIDIA’s Isaac GR00T 1.7 is one of the most compelling choices right now. It is explicitly designed for generalized robot skills, accepts vision/language/proprioception, produces action sequences, and is designed to adapt across robot embodiments.

But I would not have GR00T directly manage the entire fleet.

A practical architecture would be:

                 Human / AI Mission Planner
                           │
                 "Inspect warehouse zone B"
                           │
                 Fleet Orchestrator
             ┌─────────────┼─────────────┐
             ▼             ▼             ▼
          Robot A       Robot B        Robot C
          AMR           Arm            Quadruped
             │             │              │
           Nav2 /        VLA /          VLA /
         controller     controller     controller
             │             │              │
          motors        actuators       actuators

• Fleet layer: ROS 2 + a fleet/task allocator. It decides which robot should do what, handles priorities, charging, resource conflicts, and coordination.
• Navigation/control: Nav2 for mobile robots. It provides production-oriented perception, planning, localization, obstacle avoidance, and behavior orchestration.
• Embodied AI: GR00T or another VLA handles things like visual understanding, manipulation, and translating high-level tasks into robot skills.
• Safety/control layer: deterministic controllers, collision checking, limits, watchdogs, and emergency stops should remain below the AI.

Why I wouldn't use an LLM as the fleet controller

A frontier language model can be excellent at planning and reasoning, but it isn't a good substitute for a 100-Hz safety-critical controller. Recent robotics research is explicitly exploring LLMs controlling very different robot bodies, including arms, quadrupeds, humanoids, and physical robots—but generally by giving the model higher-level control over specialized robot policies rather than asking it to directly generate motor commands.

For a genuinely heterogeneous fleet, I'd therefore think in terms of:

LLM/VLM → fleet planner → robot-specific policy → deterministic controller

rather than:

LLM → motors

If you're building this yourself

I'd seriously consider:

ROS 2 + Nav2 + a fleet-management layer + GR00T/VLA + simulation in NVIDIA Isaac

GR00T is particularly interesting because NVIDIA's current stack explicitly supports adapting models to different hardware through post-training, while its ecosystem includes simulation, data pipelines, inference, and deployment tooling.

If you tell me what kinds of robots are in your fleet (e.g. 20 wheeled AMRs + 5 arms + 2 humanoids), their compute hardware, and whether you need real-time autonomous control or high-level task allocation, I can propose a concrete architecture and model choices.

Controlling a heterogeneous fleet of different robots (such as a mix of wheeled mobile robots, quadrupedal walkers, robotic arms, and aerial drones) requires a multi-layered AI approach rather than a single monolithic model.

Because different robot bodies have completely unique actuation spaces, kinematics, and local sensing constraints, the industry relies on a two-tier architecture : a high-level Multi-Agent Foundation Model for fleet coordination and task allocation, coupled with low-level Vision-Language-Action (VLA) or task-specific models running on individual robot form-factors.

The leading frameworks, architectures, and models used for this problem include:

1. Amazon DeepFleet (Multi-Agent Foundation Models)
   • What it is: A specialized suite of multi-agent foundation models developed specifically to predict interactions, route congestion, and manage task assignments across massive, varied mobile robot fleets.
   • How it works: It uses architectures like robot-centric (RC) decision transformers and graph-floor (GF) models to handle asynchronous updates and spatial interactions between different mechanical agents.
   • Best for: Large-scale spatial planning, traffic management, and predictive pathing across mixed robot topologies.
2. NVIDIA Isaac Platform & Omniverse Multi-Agent Frameworks
   • What it is: The premier ecosystem for simulating, training, and deploying heterogeneous robot fleets using shared AI workflows.
   • How it works: Rather than a single black-box model, NVIDIA leverages modular foundation models (like FoundationPose for tracking, Cosmos , and spatial-temporal transformers) within NVIDIA Isaac Sim and Omniverse. This allows an overarching fleet manager to orchestrate diverse morphologies (legged, wheeled, aerial) by translating high-level commands into safe, physics-simulated local trajectories.
   • Best for: End-to-end simulation, digital twinning, and bridging high-level fleet logic with hardware-accelerated local execution.
3. Vision-Language-Action (VLA) Models (e.g., RT-X / OpenVLA)
   • What it is: Open-source and proprietary cross-embodiment VLA models trained on multi-robot datasets.
   • How it works: These models ingest human language commands (e.g., "clean up the debris in sector B" ) alongside camera feeds, translating them into joint or velocity actions. Cross-embodiment models are trained across disparate robot morphologies so the same underlying model family can adapt its output tokens to different robot physical action spaces.
   • Best for: Translating high-level semantic intent into actionable multi-robot behaviors.

Summary Strategy

If you are building a control stack today, the industry standard is to use an orchestration layer powered by graph neural networks or multi-agent transformers (similar to DeepFleet 's approach) to handle routing, scheduling, and conflict resolution, which then passes generalized action targets down to localized embodied AI/VLA models running on the individual heterogeneous robots.

To help narrow down the right architectural choice, tell me:

• What types of robots are in your fleet (e.g., AMRs, robotic arms, humanoids, drones)?
• Is the environment structured (like a warehouse) or unstructured/dynamic (like outdoors or disaster response)?

Controlling a heterogeneous fleet of different robots (AGVs, robotic arms, mobile manipulators, and humanoids) isn't handled by a single "all-knowing" neural network running every actuator. Instead, the industry relies on a hierarchical architecture: a Multi-Agent Foundation Model at the high-level orchestration layer paired with Vision-Language-Action (VLA) models at the individual robot level.

The top options and approaches for multi-robot fleet control break down as follows:

• DeepFleet (by Amazon) : A pioneering suite of multi-agent foundation models specifically built for large-scale mobile robot coordination. Trained on real-world movement data from hundreds of thousands of diverse fulfillment robots, it uses architectures like decision transformers to predict congestion, optimize task allocation, and route mixed robot fleets dynamically.
• Google DeepMind Robotics / RT-X Series : Excellent for the individual "brains" of heterogeneous robots. Open-X Embodiment training allows models like RT-2 or newer iterations to take high-level natural language commands ("clean the floor" or "move that box") and translate them into actionable control primitives across entirely different robot morphologies.
• Orchestration Platforms (e.g., InOrbit, ROS 2 / Open-RMF) : While not single AI "models," vendor-agnostic orchestration layers like Open-RMF (Open-RMF via Open Robotics) or commercial systems like InOrbit act as the central nervous system. They ingest telemetry via MQTT/Kafka, prevent traffic deadlocks between disparate vendor hardware, and interface with top-tier LLMs/VLMs for natural language dispatching.

Recommended Hybrid Architecture

1. The Orchestrator (Macro-Level): Use a multi-agent routing model or fleet manager (such as DeepFleet principles or Open-RMF) to handle traffic, collision avoidance, and battery/task logistics across different machines.
2. The Translator (Mid-Level): Use a Vision-Language Model (VLM) like GPT-4o or Gemini to parse human intent or high-level site goals into standard API tasks.
3. The Actor (Micro-Level): Deploy localized Vision-Language-Action (VLA) models or behavior trees on each distinct robot to manage physical execution safely.

To give you the most precise recommendation, tell me:

• What types of robots are in your fleet (e.g., mobile bases, drones, robotic arms)?
• Is this for an indoor/warehouse environment or outdoor?
• Are you looking for an open-source framework or a commercial platform?

If by “controlling a fleet of different robots” you mean a heterogeneous fleet—e.g. wheeled AMRs, robotic arms, drones, humanoids, and custom machines—the best answer in 2026 is not one model doing everything. I’d build a hierarchical system.

My top choice: Gemini Robotics 2 + Gemini Robotics ER 2

Google DeepMind’s Gemini Robotics 2 is currently the strongest fit I’d investigate first for heterogeneous physical agents.

• Gemini Robotics ER 2 = fleet-level brain. It handles spatial reasoning, multi-step planning, task orchestration, progress monitoring, and can coordinate different robots. Google explicitly describes it as a high-level brain that can hand execution off to lower-level robot controllers.
• Gemini Robotics 2 = robot-level action model. It translates vision/language into motor control and is designed to adapt across different robot embodiments, including arms and humanoids.
• It specifically supports multi-robot collaboration, where different robot types divide up a task.
• Gemini Robotics On-Device 2 is the interesting piece if latency, connectivity, or privacy requires inference on the robot rather than in the cloud.

So I'd architect it roughly like:

                 HUMAN / MISSION
                       │
                       ▼
              Gemini Robotics ER 2
             Fleet-level reasoning
                       │
          ┌────────────┼────────────┐
          ▼            ▼            ▼
       Robot A      Robot B      Robot C
          │            │            │
     VLA / API     VLA / API     VLA / API
          │            │            │
       ┌──┴──┐       ┌─┴───┐       └───┐
       Arm   Base     Drone  Humanoid   ...

The crucial distinction is that ER 2 shouldn't directly be your motor controller. Let it decide what should happen and which robot should do it, while specialized controllers/VLAs handle the millisecond-level details.

The other serious contenders

NVIDIA Isaac GR00T is particularly compelling if you're building the infrastructure yourself. It combines a robot foundation model with data pipelines, simulation, middleware, CUDA acceleration, and Jetson deployment rather than being merely an AI model.

π0 from Physical Intelligence is also very interesting: it was trained across multiple robot platforms and is explicitly designed as a generalist vision-language-action policy.

What I'd actually build

For a serious fleet, I'd use:

ER 2 → fleet orchestration → robot-specific VLA/controller → ROS 2 → safety controller

rather than trying to have a giant LLM directly drive every actuator.

That gives you a separation between:

1. Mission reasoning: “Unload these 40 boxes.”
2. Task allocation: “Robot 7 handles pallets; robot 12 handles sorting.”
3. Planning: trajectories, navigation, grasp selection, etc.
4. Low-level control: motors, joints, brakes, collision avoidance.
5. Safety: independent hard real-time limits that AI cannot override.

If you tell me what kinds of robots are in your fleet (e.g. AGVs + arms + drones, humanoids, construction robots, etc.), I can recommend a much more specific architecture—including which model should run where, hardware requirements, ROS 2 design, communication protocol, and how I'd coordinate 10/100/1,000 robots.

The other serious contenders

NVIDIA Isaac GR00T is particularly compelling if you're building the infrastructure yourself. It combines a robot foundation model with data pipelines, simulation, middleware, CUDA acceleration, and Jetson deployment rather than being merely an AI model.

π0 from Physical Intelligence is also very interesting: it was trained across multiple robot platforms and is explicitly designed as a generalist vision-language-action policy.

What I'd actually build

For a serious fleet, I'd use:

ER 2 → fleet orchestration → robot-specific VLA/controller → ROS 2 → safety controller

If by “controlling a fleet” you mean one AI system coordinating different robot types—e.g. mobile bases, arms, humanoids, drones, etc.—I would not use a single end-to-end model as the fleet controller.

My pick: NVIDIA Isaac GR00T + a fleet-level reasoning layer

For a heterogeneous fleet today, NVIDIA Isaac GR00T is probably the strongest starting point if you want something you can actually customize and deploy. GR00T is explicitly designed for cross-embodiment robot skills, accepts vision/language/proprioception, and can be adapted to new hardware with additional demonstrations. GR00T N1.7 is also commercially licensable under Apache 2.0.

I'd structure the system roughly like this:

                 Fleet AI / Mission Planner
                         │
          ┌──────────────┼──────────────┐
          ▼              ▼              ▼
       Robot A        Robot B        Robot C
      humanoid       mobile arm       drone
          │              │              │
      GR00T/VLA      GR00T/VLA      specialized
          │              │              │
       low-level controllers / ROS 2

The important distinction is that the fleet AI decides what should happen, while each robot's VLA/controller decides how that particular body performs it.

The other serious contender: Gemini Robotics

Google DeepMind Gemini Robotics 2 is arguably the more impressive choice if your priority is general reasoning and heterogeneous robot interaction rather than openness. Google explicitly describes Gemini Robotics 2 as supporting different robot embodiments and multi-robot collaboration, with a separate Gemini Robotics ER 2 model handling embodied reasoning and multi-step planning.

So my ranking would be:

One important caveat: today's robot foundation models are not yet a solved universal fleet brain. Stanford's 2026 AI Index notes that VLAs remain largely at the research stage and that the gap between controlled demonstrations and reliable real-world operation is still substantial.

If you're actually building a heterogeneous robot fleet, tell me what types of robots you have (e.g. 10 AMRs + 5 arms + 2 humanoids) and what the fleet needs to accomplish. I can recommend a concrete architecture—including model, ROS 2, simulation, networking, fleet scheduler, inference hardware, and how the robots should communicate with each other.

When controlling a heterogeneous fleet of different robots (e.g., a mix of AMRs, robotic arms, quadrupeds, and humanoids from different vendors), there is no single "all-in-one" AI model that directly drives every motor and joint across completely different form factors. Instead, the industry relies on a hierarchical architecture combining high-level Vision-Language-Action (VLA) or Large Language Models (LLMs) for fleet orchestration, coupled with standardized middleware and hardware-specific foundation models.

The leading ecosystems and approaches for managing mixed robot fleets break down as follows:

• NVIDIA Isaac Sim & Omniverse / GR00T Stack:
  • Why it leads: NVIDIA has effectively built the dominant platform infrastructure for physical AI and multi-vendor robot simulation and control. Using frameworks like Isaac Lab and support for standards like VDA 5050 , companies use NVIDIA's ecosystem to link different robotic form factors (wheeled, arms, humanoids) into a unified digital twin and orchestration pipeline.
• Centralized LLM / VLA Orchestration Frameworks (e.g., RobotFleet / Open-Source Architectures):
  • Why it's emerging: Recent academic and enterprise frameworks use generalist high-level reasoners (like top-tier LLMs/VLAs) as a centralized "brain" to translate plain-language business goals into sub-tasks for diverse hardware. These models maintain a shared declarative world state and assign tasks down to vendor-agnostic containerized robot services.
• VDA 5050 Standard + Vendor-Agnostic Fleet Management (e.g., Kinexon / idealworks / Agility Arc):
  • The practical industry standard: For mixed mobile robot fleets (AMRs and AGVs), the "best model" isn’t a neural network alone, but an architecture adhering to the VDA 5050 communication standard , allowing vehicles from different makers to talk to a centralized AI-optimized fleet manager seamlessly.

Choosing the Right Approach

1. For High-Level Orchestration & Task Planning: Use an LLM/VLA-based central dispatcher to handle natural language instructions, environment constraints, and multi-robot cooperation.
2. For Interoperability & Navigation: Rely on standardized communication protocols (VDA 5050 ) combined with hardware-agnostic simulation frameworks like NVIDIA Omniverse Blueprint to prevent vendor lock-in.

To help narrow down the ideal stack, tell me:

• What types of robots are in your fleet (e.g., mobile AMRs, robotic arms, drones)?
• Are they from the same manufacturer or multiple different vendors?
• What is the primary environment (e.g., structured warehouse, chaotic factory floor)?

When controlling a heterogeneous fleet of different robots (e.g., mobile AGVs, robotic arms, quadrupeds, and humanoids), there is no single "all-in-one" AI model that directly twitches every motor across entirely different physical bodies. Instead, the industry relies on a hierarchical hybrid architecture where different layers of AI collaborate.

The most effective approach divides control into High-Level Orchestration (the Fleet Brain) and Low-Level Execution (the Robot Body).

Phase 1: High-Level Orchestration (Fleet Brain)

To manage task allocation, natural language commands, traffic routing, and cross-platform cooperation, Multimodal Large Language Models (MLLMs) and Vision-Language-Action (VLA) frameworks serve as the top-level coordinators.

• Top Choice: GPT-4o / Claude 3.5 Sonnet (for Semantic Orchestration) or Open-source alternatives like Llama 3 Vision
  • Why: At the fleet level, you need deep common sense, reasoning, and context understanding. If a human operator says, "The assembly line is out of bracket-type B, send a mobile robot to the warehouse and have the robotic arm load it," an MLLM translates this high-level intent into structured API calls, breaking tasks down per robot type.
• Emerging VLA Frameworks: Hierarchical models like τ₀-VLA or customized OpenVLA architectures are increasingly used to bridge high-level task plans with cross-robot execution by using unified flow-matching policies.

Phase 2: Mid-Level Coordination & Traffic (Multi-Agent Navigation)

Different robots moving through the same space create exponential congestion. Classical fleet management algorithms fail when robot densities and types vary wildly.

• Top Choice: Hybrid Reinforcement Learning + Graph Neural Networks (GNNs)
  • Why: Recent 2026 research from institutions like MIT and industrial automation leaders shows that learning-based spatial routing models achieve up to 25% greater throughput than traditional algorithmic grid-locks. GNNs excel at mapping heterogeneous nodes (different robot speeds, sizes, and turning radiuses) into a unified traffic controller.

Phase 3: Low-Level Control (The Robot Body)

An LLM cannot natively handle 100Hz joint torques. Every individual robot in the fleet must still run its own localized edge intelligence.

• Top Choice: ROS 2 (Robot Operating System 2) paired with End-to-End Visual-Motor Policies or specialized inverse kinematics.
  • Why: ROS 2 acts as the middleware communication fabric. While the cloud/edge "fleet brain" passes goal coordinates or high-level directives down, each distinct robot uses localized models (like diffusion policies or reinforcement-learned locomotion models) tailored strictly to its hardware anatomy. [1][2]

Summary Architecture Recommendation

If you are designing a heterogeneous fleet control system today, use a three-tier stack:

1. Orchestration Layer: A powerful Multimodal LLM / VLA to parse human instructions, log data, and assign macro-tasks.
2. Routing Layer: A Neural Combinatorial Optimization / Multi-Agent RL engine to handle traffic, collision avoidance, and spatial scheduling across mixed robot types.
3. Execution Layer: ROS 2 running device-specific neural policies or classical controllers on each individual machine. [1][2][3][4][5]

If you'd like to narrow this down, tell me:

• What specific types of robots are in your fleet (e.g., drones, wheeled AGVs, robotic arms)?
• Are they operating in a structured environment (like a warehouse) or an unstructured one?

I can help you map out a more specific software stack or integration framework.

If you mean one AI system that can coordinate a heterogeneous fleet—e.g. mobile robots, robotic arms, humanoids, drones, AGVs/AMRs, etc.—my current pick would be Google DeepMind’s Gemini Robotics 2 family, but not as the sole controller.

My recommendation

Best overall architecture:

                Fleet / Mission Manager
                         │
                Gemini Robotics ER 2
              (reasoning + planning)
                         │
          ┌──────────────┼──────────────┐
          ▼              ▼              ▼
       Robot A        Robot B        Robot C
       VLA policy     VLA policy     VLA policy
          │              │              │
       ROS 2 /       ROS 2 /       ROS 2 /
       controller    controller    controller

The important distinction is that fleet coordination and low-level robot control are different problems.

• Gemini Robotics ER 2 → high-level reasoning, spatial understanding, task decomposition, planning, coordination and tool/function calls.
• Gemini Robotics 2 → vision-language-action control, translating intent into physical actions.
• Robot-specific controllers → hard real-time motion, balance, collision avoidance, joint control, navigation, safety interlocks, etc.

Google specifically describes Robotics 2 as supporting different robot embodiments and multi-robot collaboration, including robots communicating and dividing work.

Why I'd choose it for a heterogeneous fleet

The killer feature isn't simply "best vision model." It's cross-embodiment generalization.

Google reports that its robotics models can transfer learned behaviors between substantially different platforms. Earlier Gemini Robotics 1.5, for example, demonstrated transfer among ALOHA 2, Franka and Apptronik's Apollo humanoid.

That's unusually relevant if your fleet looks something like:

50 AMRs + 10 arms + 5 humanoids + inspection drones rather than 50 identical robots.

The main alternative: NVIDIA

If your priority is owning the stack and running it yourself, I'd look very seriously at NVIDIA's robotics ecosystem, particularly GR00T + Cosmos + Isaac.

NVIDIA's GR00T N1.6 is an open foundation VLA model aimed at generalist humanoid robots, while the broader NVIDIA stack provides simulation, synthetic data, perception/reasoning and deployment infrastructure.

So I'd roughly rank the approaches:

One important caveat: I would not let a foundation model directly command motors or safety-critical actuators. Put it above deterministic navigation, motion planning, collision avoidance and safety systems. The AI should decide what should happen; conventional robotics software should constrain and execute how it physically happens.

If you tell me what kinds of robots are in your fleet (e.g. drones, humanoids, arms, AMRs), how many, and whether they need to operate autonomously/offline, I can give you a concrete architecture and recommend the specific models/hardware I'd use.

If by “controlling a fleet of different robots” you mean something like 10–1,000 robots of different embodiments—AMRs, arms, quadrupeds, humanoids, drones—with one AI system coordinating what they do, I would not pick a single end-to-end model and have it directly drive every motor.

My pick in 2026: Gemini Robotics ER 2 + robot-specific policies

For the fleet-level intelligence, Google DeepMind’s Gemini Robotics ER 2 is probably the most interesting choice right now. Google explicitly designed it as a high-level embodied-reasoning model that can do spatial reasoning, multi-step planning, progress monitoring, tool use, and multi-robot orchestration, while handing actual motor execution to lower-level VLA/control policies.

The architecture I'd use is roughly:

                    ┌──────────────────────────┐
                    │   Fleet Intelligence      │
                    │   Gemini Robotics ER 2    │
                    │                          │
                    │ • Understand mission      │
                    │ • Decompose tasks         │
                    │ • Allocate robots         │
                    │ • Monitor progress        │
                    │ • Re-plan / recover       │
                    └────────────┬─────────────┘
                                 │
                     ROS 2 / fleet API / tools
                                 │
              ┌──────────────────┼──────────────────┐
              ▼                  ▼                  ▼
        ┌──────────┐       ┌──────────┐       ┌──────────┐
        │   AMRs   │       │   Arms   │       │ Humanoids│
        │ Nav policy│       │ VLA      │       │ VLA      │
        └──────────┘       └──────────┘       └──────────┘
              │                  │                  │
        local planner       motion control     whole-body control
              │                  │                  │
              └────────────── safety layer ────────┘

That's much more scalable than asking one giant model to output joint commands for every robot.

The strongest alternative: NVIDIA's stack

If your priority is actually deploying this commercially, rather than choosing the theoretically smartest model, I'd seriously consider NVIDIA's Isaac ecosystem.

NVIDIA's current stack combines Isaac ROS/ROS 2, Isaac Sim, Isaac Lab, foundation models such as GR00T, accelerated perception and motion planning, and on-robot inference. Importantly, NVIDIA explicitly supports autonomous mobile robots, arms, manipulators and humanoids rather than focusing solely on one embodiment.

Their GR00T N1.7 is particularly interesting if many of your robots are manipulators/humanoids: it's an open, commercially licensable VLA designed for cross-embodiment generalization and can be fine-tuned to particular robots and tasks.

So I'd rank the approaches like this

Google's newer Gemini Robotics 2 is also notable because it is explicitly aimed at whole-body control, dexterity, teamwork, and adaptation across different robot bodies; its on-device version is intended to run locally on robots.

The key insight

The best “robot fleet model” isn't actually one model.

I'd build a hierarchical robotics AI system:

1. Fleet model — reasons about the entire environment and mission.
2. Task planner — converts goals into executable subtasks.
3. Robot-specific VLA/policy — turns subtasks into actions appropriate for that embodiment.
4. Classical robotics stack — navigation, trajectory planning, IK, grasping, etc.
5. Hard safety controller — prevents the AI from commanding unsafe motion.
6. Fleet scheduler — handles resources, collisions, charging, priorities and failures.

That separation is important because an LLM/VLM is good at deciding “send robot 17 to inspect aisle 4”, whereas a 1-kHz controller is better at deciding “what torque should joint 3 receive right now?”

If you tell me what kinds of robots are in your fleet (e.g. Boston Dynamics, Unitree, drones, AMRs, robot arms), how many, and whether you want cloud or on-device control, I can recommend a concrete architecture—including the model, ROS 2 setup, GPUs, APIs, and how I'd train/fine-tune it.

That's much more scalable than asking one giant model to output joint commands for every robot.

The strongest alternative: NVIDIA's stack

If your priority is actually deploying this commercially, rather than choosing the theoretically smartest model, I'd seriously consider NVIDIA's Isaac ecosystem.

NVIDIA's current stack combines Isaac ROS/ROS 2, Isaac Sim, Isaac Lab, foundation models such as GR00T, accelerated perception and motion planning, and on-robot inference. Importantly, NVIDIA explicitly supports autonomous mobile robots, arms, manipulators and humanoids rather than focusing solely on one embodiment.

Their GR00T N1.7 is particularly interesting if many of your robots are manipulators/humanoids: it's an open, commercially licensable VLA designed for cross-embodiment generalization and can be fine-tuned to particular robots and tasks.

So I'd rank the approaches like this

Google's newer Gemini Robotics 2 is also notable because it is explicitly aimed at whole-body control, dexterity, teamwork, and adaptation across different robot bodies; its on-device version is intended to run locally on robots.

The key insight

The best “robot fleet model” isn't actually one model.

I'd build a hierarchical robotics AI system:

When it comes to controlling a heterogeneous fleet (a mix of different robots like drones, wheeled AGVs, robotic arms, and humanoids), there is no single "drop-in" neural network that directly handles low-level motor controls for every distinct machine shape out of the box. Instead, the industry relies on a hierarchical architecture combining high-level Vision-Language-Action (VLA) foundation models for reasoning with specialized multiemembodiment frameworks for execution.

The top-performing frameworks, models, and architectures used to orchestrate diverse robot fleets include:

1. The High-Level Orchestrator: Vision-Language-Models (VLMs) & LLMs

At the fleet management layer, high-level models act as the "dispatcher" or "brain" that translates natural language commands or overall mission goals into sub-tasks for specific robots.

• OpenAI (GPT-4o) / Anthropic (Claude 3.5 Sonnet): Frequently used in research and enterprise middleware to ingest camera feeds or facility states from a fleet, reason about spatial layouts, and write ROS 2 (Robot Operating System) navigation or task goals tailored to specific robots (e.g., telling a drone to inspect a leak and a wheeled robot to clean it up).
• Google's RT-X (Robotics Transformer-X) Family (RT-1-X, RT-2-X): Built on the Open X-Embodiment dataset, RT-X was trained across 22 different robot types . It is one of the premier cross-embodiment generalist models, meaning a single high-capacity model can transfer learned manipulation and movement skills from one robot morphology to an entirely different physical form.

2. The Multi-Embodiment Execution Layer: Generalist Policies

For direct sensor-to-action control across diverse hardware, generalist action models map diverse inputs to distinct robot kinematics:

• Physical Intelligence's π₀ (Pi0): A state-of-the-art Vision-Language-Action flow model designed specifically to handle generalized robot manipulation across varied, fluid real-world settings. It adapts fluidly to different physical interfaces compared to rigid, single-robot coded routines.
• Octo: An open-source, generalist robotic policy trained on the Open X-Embodiment dataset that can be prompted or quickly fine-tuned to control custom or off-the-shelf robotic hardware configurations.

3. The Fleet Infrastructure & Simulation Backbone: NVIDIA Isaac / Omniverse

No AI model runs a multi-vendor fleet in isolation; it requires a unifying operating system.

• NVIDIA Isaac Sim & Omniverse: This platform serves as the digital twin backend for heterogeneous fleet coordination. It allows multi-agent foundation models to orchestrate airborne, bipedal, and wheeled systems in real-time shared simulations before pushing commands to physical hardware.
• ROS 2 Middleware: Paired with AI layers, ROS 2 remains the non-negotiable communication fabric translating generalized AI outputs into device-specific driver commands.

Summary Recommendation

• For reasoning & task allocation across different robots: Use an advanced multimodal model like GPT-4o or Claude combined with custom task planners.
• For cross-platform physical manipulation/action: Leverage open generalist weights like Octo or π₀ trained on Open X-Embodiment data.
• For the overall ecosystem/fleet management infrastructure: Build on top of NVIDIA Isaac / Omniverse to handle the multi-agent digital twin coordination.

If you want to narrow this down, tell me:

• What types of robots are in your fleet (e.g., mobile bases, robotic arms, drones)?
• Are you looking for cloud-based fleet coordination or edge-computed local autonomy?