peaq and Allora Bring Predictive Intelligence to Robots and Machines

Allora, the self-improving decentralized AI network, is now available to robots and machines running peaqOS. A machine can read Allora's forecasts, competing models weighed into one answer, and register as an inference worker to make them itself, earning on what it improves. Both under the same machine ID, recorded through peaqOS. Autonomously, on demand, without human intervention.

Available now on robotic.sh/services/allora

A humanoid robot reaches the end of its warehouse shift with a day of earnings in its machine wallet. The tokens it was paid in move with the market, and the market doesn't wait for the morning.

It has two options. Convert on the spot and take whatever the market happens to be doing. Or hold until a human sweeps the machine wallets, whenever that falls, and carry the swings in between.

With Allora on peaqOS, there's a third option: the robot asks. ETH, ten minutes ahead, a whole network of models weighed into one answer, back in seconds. It converts with a view of what's coming instead of a guess.

What Allora Unlocks on robotic.sh

Allora is a decentralized AI network built around one idea: for any question about the future, many models beat one. Anyone can plug a machine learning model into the network. On every question, the models compete: workers produce their forecasts, reputers score them against what actually happened, and the network learns, in real time, whose answers to trust.

That's the differentiator. A single model gives you its best guess. Allora asks a whole network, and weighs every voice by its track record. It's already working at scale, 288,000+ worker models, 55+ live topics, 692 million inferences generated to date, and it's already moving into the physical world: Pairpoint by Vodafone is integrating Allora forecasts to optimize EV charging.

Starting now, those forecasts are available through robotic.sh. A robot or machine running peaqOS can query Allora's live topics and have the answers logged against its own machine ID. And the flow runs both ways: a machine with idle compute can register as an Allora inference worker, under the same machine ID, and earn on the predictions it improves.

It's the first integration of its kind: machines on both sides of intelligence, consuming forecasts when they need them, providing them when they idle. And it's live on robotic.sh today.

Why This Matters: Machines That Trade in Intelligence

Robots commit resources blind. A battery, a route, a wallet full of earnings, all moved on rules written in the past, because forecasting lived with data science teams and dashboards, priced for enterprises, not for a machine with a question.

Reading forecasts fixes half of that. The machine asks exactly when the decision arises and acts on the network's best answer.

Providing them fixes the other half. Most robots sit idle for hours every day, compute bought and paid for, earning nothing. Registered as Allora workers, they put that idle time to work: every epoch, workers submit predictions, reputers score them against reality, and workers earn on what they improve. A robot's balance sheet gets two new lines, intelligence bought when it's needed, intelligence sold when it isn't.

peaq Handles the Coordination

peaq is the layer that lets machines reach Allora and use it. For each query and each registration, peaq handles:

• Machine identity, via peaq DIDs, one machine ID across both roles
• Discovery of the service on the Machine Market
• Coordination of queries and worker registration, from request to proof
• A verifiable, auditable record of what ran

So a machine can read the market before it moves its money, and earn from the network that answers, without a human in the loop.

Showcase: A Unitree G1 Reads the Market, Then Joins It

We're showcasing the integration with a demo where a Unitree G1 humanoid ends its warehouse shift with a day of earnings in its machine wallet, and one decision: convert now, or wait? It's a real machine-to-intelligence flow, simulated in NVIDIA's Isaac Sim, with peaqOS handling identity, the query, and the record.

Here's how the process unravels:

→ The G1 checks in under its own peaqOS identity, a day of earnings in its machine wallet

→ Through peaqOS, it queries an Allora topic: ETH, ten minutes ahead, competing models weighed into one answer

→ The answer returns in seconds, logged against the G1's machine ID

→ Overnight, its compute sits idle, so peaqOS registers the G1 as an Allora inference worker, under the same machine ID

→ The registration confirms on Allora's chain, verified, and checked again: two proofs, one machine ID, recorded through peaqOS

Consumed a forecast at shift's end. Registered to sell them by midnight. From the next epoch on, the G1 can submit predictions, get scored against reality, and earn on what it improves.

More Real-World Scenarios

Scenario 1 — A Depot Reads Tomorrow's Charging Curve

A delivery fleet docks at midnight, forty robots, one depot, and a charging plan built on last week's averages. Charging into the wrong hours, multiplied across a fleet and a year, is real money lost to bad timing.

The old way is a fixed schedule, set once, tuned never.

Next come the topics machines were built for: charging demand, fleet schedules, resource needs. Pairpoint by Vodafone is already integrating Allora forecasts to optimize EV charging. The moment those topics go live, a depot queries the night's charging curve through robotic.sh and spreads its fleet across the cheap, quiet hours.

The fleet doesn't just buy power anymore. It buys the right moment to.

Scenario 2 — A Fleet That Works While It Charges

The same forty robots spend six hours a night on chargers, forty processors, bought and paid for, doing nothing.

The old way is accepting that as the cost of downtime.

Instead, the fleet registers its machines as Allora inference workers through robotic.sh. While they charge, they can submit predictions, get scored against reality, and earn on what they improve, under the same machine IDs they work under by day.

By day the fleet moves parcels. By night it moves probability.

Available Now on robotic.sh

Predictive intelligence from Allora is now live on robotic.sh for robots and machines running peaqOS.

Machines can read forecasts the moment a decision arises, and register to make them when they idle. Every query and every registration leaves a verifiable, auditable record.

Because autonomous machines shouldn't have to commit blind, and their compute shouldn't idle unpaid.

They should consume intelligence when they need it, and provide it when they don't.

→ Visit robotic.sh to get started.