At Formant, we live and breathe robotics—and we’ve learned one thing above all: reality always wins. It’s the constant backdrop, the thing every system ultimately answers to. And in robotics, where steel meets soil, that truth shows up fast.

While much of the AI world lives in tokens and artistic/textual output, we see things in a harsher light. Our platform orchestrates and analyzes fleets of robots—machines navigating farms, inspecting factories, or operating in harsh, offline environments. For them, success isn't stylistic. It's: Did it get the job done? No hallucinations allowed. The AI either worked or it didn’t. That’s why we believe: AI that isn’t grounded in reality isn’t ready.

This article is a look under the hood—how we think about “reality alignment,” how different parts of AI connect with the world, and why orchestrating these systems is the only way forward.

AI as Systems of Captured Reality

Every AI system begins with a slice of the real world someone recorded.

LLMs? They're trained on the accumulated insights of human civilization that made it to the internet. Think of them as massive, frozen museums of thought—books, codebases, dialogues—all documented reality at scale. Nobody writing 500 years ago knew their thoughts would someday help a robot diagnose a broken valve, but here we are.

RAG (Retrieval-Augmented Generation) narrows the lens. These systems don’t just know the world in general—they go looking for the exact document, markdown file, or spec sheet that matters right for work today. For robotics, this means AI that can say: “Here’s the exact torque tolerance for this bolt on this robot in this factory.” That description of reality, pre-selected as relevant and on demand.

Specialized neural networks keeping your robot from tipping over? These networks are trained using vast amounts of sensor data collected from robots operating in controlled lab environments. This data, encompassing everything from joint angles and IMU readings to force-torque sensor outputs and camera feeds, allows the neural networks to learn the complex dynamics of the robot and how to react to various disturbances in real-time.

And then there’s user memory. These systems track what users care about—what questions get asked, what patterns emerge, what failures repeat, and what they explicitly tell you they care about. They don’t just learn from data—they learn from you directly or indirectly. The net result? AI that doesn’t invent from scratch. It remembers, retrieves, and reflects the world it’s been shown before.

Feedback from the Field

Static memory isn’t enough. Robots move. People’s values change. We all know that the world changes and that AI (like us) must adapt to continue to remain relevant.

What makes robotics so brutally effective as an AI testing ground is this: reality gives you a grade immediately. Did the arm pick the right apple? Did the drone fly the route? Did the inspection actually detect the defect? Unlike a paragraph that might have a hallucination inside it unnoticed by many, a robot operation cannot defy reality in the middle of its tasks.

At Formant, we help manage countless of these moments every day. Our platform tracks the outcome of real actions in physical space as events. It’s not just “what did the model say?” It’s “did it work out there?” That feedback loop—sensor data, operator input, mission task metrics—is how automation improves. Not through guesswork, but through measurement of the world.

Foundation models give you a head start. But feedback from the field is what tunes an AI from generalist to specialist—from plausible to actually useful.

The Dance of Onboard and Offboard AI

In robotics, intelligence isn’t centralized. As mentioned in our last article, A Symphony of AI Orchestration, it’s a team effort between what runs on the robot and what runs off it. Each has a role to play.

Onboard AI is the robot’s reflexes. It keeps it upright, obstacle-free, moving through the world. It’s fast, lean, and local—trained directly on sensor data. It can’t afford to think long-term; it needs to react now.

Offboard AI is more like mission control. It sees the bigger picture—multi-day plans, optimization strategies, and deep diagnostics. It’s where we plug in LLMs, feedback analysis, and orchestration logic. These systems might not move the wheels, but they decide where the wheels should go.

Together, they form a complete brain: reactive and reflective, fast and thoughtful. One handles survival; the other handles strategy. And both depend on real-world context to work.

Humans as Part of Reality

We all know reality isn’t just rocks and robots. It’s also human values, goals, and priorities.

A robot can complete 10 tasks, but if none of them save money, hit a KPI, or make a customer smile, it hasn’t succeeded. Humans define success—and their feedback is some of the most important “reality” we deal with.

That’s why Formant captures not just sensor data, but human judgment. Whether it’s a roboticist tweaking code, a farmer checking uptime, or an analyst tracking ROI, their judgments complete the loop. AI that ignores human values might be “technically correct”—but useless in practice on top of wasting money on inappropriate problems.

And here’s where it gets interesting: different humans care about different things. Speed, safety, cost, compliance, UX. All can be valid at different times. All shifting. That’s why we don’t build one-size-fits-all systems. We orchestrate multiple AI personas that adapt to the values at play in each organization.

Final Word

The frontier of AI isn’t some abstract intelligence floating in a cloud simulator. It’s here—on the ground, embedded in machines, judged by what it does by opinionated people.

At Formant, we’re building AI systems that don’t just think—they interact, analyze, take human feedback, adapt, and evolve based on the most unforgiving teacher there is: the real world.

So yes, we’re excited about GPTs, retrieval, memory, and all the rest. But what gets us up in the morning? Watching a robot take a suggestion from an AI and do something better than it did yesterday. That’s the win we’re looking for.