Building robots for unpredictable, infrastructure-free environments

Burros can carry, tow, scout, patrol, mow, push, pull, or propel a variety of attachments as a platform for manipulation. | Credit: Burro AI In 2018, Burro did a demo. The robot worked.

We were excited. We thought we understood the problem. We did not understand the problem.

What we understood was how to make a robot perform in conditions we controlled, for an audience prepared to see it succeed, over a time horizon short enough that the long tail of real-world failures hadn’t had time to appear. That is what a demo is. It is a proof of concept for a best-case scenario.

It is not a proof of concept for Tuesday morning in November when it’s raining, and the lighting is flat, and a worker approaches from an unexpected angle, and the robot is operating in a country it has never been to before. The gap between those two things is where most robotics companies fail. Not because their technology is bad, but because they optimized for the wrong thing for too long.

They kept the demo alive while the real-world deployment problem went unsolved. Turning a demo into a product Burro made a different choice, though not because we were smarter. We made it because we had no alternative.

The environments we were working in, outdoor agricultural settings with no fixed infrastructure, no controlled lighting, and no GPS reliability under canopy. The agricultural workforce was not going to modify its behavior to accommodate a machine. All of these did not permit the kind of controlled-conditions optimization that indoor robotics can sustain for years before hitting the real world.

We had to confront the real-world problem immediately, which meant we had to start learning from it immediately. What we learned first was about tolerance. People who depend on a robot for their livelihood have zero tolerance for unreliability.

When a customer first adopts an autonomous system, they think of it as an interesting new tool. Within weeks, if the system is delivering value, their mental model shifts entirely. They are now depending on it.

They have organized their workflow around it. They have told their team to plan around it. When it fails, they are not mildly disappointed.

They are angry in the way you are angry when critical infrastructure fails, because that is what it has become. This shift from novelty to dependency happens faster than most companies expect, and the reliability bar it sets is higher than any lab environment will prepare you for. What we learned second was about environmental variability.

Nothing outdoors is static. The same row looks different at dawn, midday, and dusk. It looks different in summer and winter, in rain and sun, in dust and mud.

Temperature ranges from below freezing to 120 degrees Fahrenheit. The robot that performs reliably across all of these conditions is not a better version of the robot that performs reliably in one of them. It is a fundamentally different engineering achievement, built from exposure to those conditions over time, not from modeling them in simulation.

Save the date for RoboBusiness 2026 Working in outdoor environments in the elements The industrial outdoor environments that represent the next frontier for autonomous robotics present exactly this same problem set, with some additions. A port yard has the variability of outdoor conditions plus the complexity of heavy vehicle traffic, irregular human movement, and operations that run continuously without seasonal breaks. A logistics campus has the unpredictability of outdoor terrain plus the throughput requirements of a business that cannot absorb downtime.

A construction site has all of the above plus an environment that physically changes every day as work progresses. None of these environments can be solved from inside a lab. None of them can be solved through simulation alone, no matter how sophisticated the simulation becomes.

They can only be solved by being in them, accumulating real operational data, failing safely, learning rapidly, and iterating on that learning at fleet scale. A mistake made in one environment, absorbed into the system and corrected, makes every unit operating everywhere more reliable. That is not a theoretical advantage.

It is the only way this class of problem actually gets solved. The research foundation that would unlock the next phase of outdoor and industrial autonomous robotics is infrastructure-free localization and perception in unstructured open-world conditions. The ability to know precisely where you are and what surrounds you, maintaining that knowledge reliably as sensors degrade over time and the environment changes around you, without any supporting infrastructure, is the capability that separates systems that work in demonstrations from systems that work in the world.

This received serious research investment for indoor environments a decade ago. It has not received equivalent investment for outdoor unstructured environments, and that gap is the primary technical bottleneck remaining. The industrial automation industry has done extraordinary work inside controlled environments.

The next decade of value is outside those environments, in the yards and corridors and sites where the physical economy actually operates. The lessons for getting there are not in the research literature. They are in eight years of field operation, a dataset that nobody else has, and a very clear understanding of the difference between a demo and a deployment.

We know what that difference costs to learn. We paid for it in full. About the author Vibhor Sood is co-founder and vice president of engineering at Burro Robotics.

He builds Burro robots, and his software controls them. Sood has developed many of the computer-vision approaches to localization and autonomy at Philadelphia-based Burro. Before Burro, Sood worked as a researcher at Lehigh University’s Vader Labs, where he specialized in accurate infrastructure-free outdoor localization, and as a software engineer at Samsung.

E. S. in electrical, electronics, and communications engineering from Manav Rachna International University in Faridabad, India.

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