The next wave of AI doesn’t just answer — it acts. Local models, edge compute and actuation, built to your task. Haink supplies the hardware and builds the AI software, so one partner delivers both halves of a robotics or physical-AI system.
Physical AI is artificial intelligence that perceives and acts in the physical world. It pairs local models — vision, language and control — running on edge compute with sensors and actuators such as robotic arms and mobile robots. Haink designs and builds these systems end to end: the model, the edge hardware it runs on, and the integration that turns inference into motion.
In 2026 physical AI crossed from research into deployment — the market is put at roughly $1.5 billion in 2026, growing toward $15 billion by 2032, with manufacturers such as BMW and Toyota already in production. Four shifts drive it, and they play to a hardware-plus-software partner.
Representative deployment patterns across sectors — the demand we build for, not Haink client claims. Figures are typical 2026 industry ranges.
Vision-language-action models drive pick-and-place, assembly and machine-tending — the largest 2026 deployment area, with makers such as BMW and Toyota already in production.
AMRs and mobile manipulators move, sort and pick — perception and control running on-device at the edge.
Quadruped and fixed robots run visual inspection across hazardous, remote or round-the-clock sites.
Outdoor robots for harvesting, monitoring and material handling — trained simulation-first to cut data cost.
Five capabilities across the path from chip to motion — pick one, or take the whole stack from one partner.
Vision, language and vision-language-action (VLA) models running on-device — sized to latency and power budgets, not cloud round-trips.
Learn more →Training perception, control and VLA models in simulation and with teleoperation data, then transferring to the real machine.
Learn more →A physics-accurate virtual replica to design, train and validate before hardware moves — built on NVIDIA Omniverse and Isaac Sim.
Learn more →Connecting models to manipulators, sensors and controllers, with safety (ISO 26262, IEC 61508) and fleet updates — inference turned into reliable motion.
Learn more →Validated robotics blueprints — edge + manipulator cell, AMR, VLA inspection — each with a bill of materials and indicative pricing.
Learn more →Jetson Thor / Orin, IGX, edge servers, sensors and Isaac Sim workstations — supplied from devkit to fleet.
Browse hardware →Most vendors have one half. A robotics integrator buys models in; an AI studio buys hardware in. We already do both — so there’s no seam to manage and no finger-pointing.
An illustrative starting design for a vision-guided manipulation cell. Indicative; tailored per use case.
| Layer | Typical choice | Role |
|---|---|---|
| Edge compute | NVIDIA Jetson AGX Thor / AGX Orin (IGX for safety) | Runs perception, control & VLA models on-device, low latency |
| Perception | Industrial cameras + depth sensor | Vision input for detection and pose estimation |
| Models | Fine-tuned vision + control policy | Detect, plan, and command motion — tuned to the task |
| Actuation | Robotic arm + controller | Executes pick-and-place / manipulation |
| Connectivity | Industrial Ethernet / Wi-Fi 6 | Telemetry, updates, optional fleet coordination |
| Software | Inference runtime + MLOps | Deployment, monitoring and model updates over time |
More blueprints with bills of materials and pricing: Physical AI reference architectures → · need training-scale GPU compute? See Private AI.
Prefer not to fund it upfront? Deployments can be delivered as robotics-as-a-service (RaaS) — hardware, software and support bundled into a per-robot monthly fee. A typical pilot’s data and training stage budgets ~$50K–$150K; see deployment cost.
Physical AI is artificial intelligence that perceives and acts in the physical world — local models running on edge compute, connected to sensors and actuators such as robotic arms and mobile robots. It extends generative AI from text and images to motion and manipulation.
Haink builds both halves: an in-house senior AI/ML team develops the models and control software, and Haink supplies the edge compute and robotics hardware they run on — one accountable partner from chip to motion.
In 2026, edge inference for robotics runs on NVIDIA Jetson AGX Thor and IGX Thor for advanced and safety-critical robots, with AGX Orin and IGX Orin for lighter workloads — paired with cameras, sensors and a controller for actuation. Haink configures and supplies these as edge inference nodes — see our robotics & physical-AI hardware and Orin vs Thor guide.
A typical 2026 pilot budgets roughly $50K–$150K for the data and model-training stage, with edge hardware and integration on top; first pilots commonly land in the low-to-mid six figures, and scaling to a fleet lowers the cost per robot. Robotics-as-a-service (RaaS) can replace upfront capital with a monthly fee. See deployment cost.
We build physical-AI solutions to a client task — perception, local inference and actuation — on hardware we supply, and we’re also exploring our own products. Engagements start with a consultation to scope the use case.
It’s a new and expanding practice. We’re building it deliberately, with real customer problems. If physical AI is on your roadmap, an early conversation helps shape what we deliver.
Tell us the task you have in mind — we’ll tell you honestly what’s feasible today and design a path from prototype to deployment.