Frontier · Robot-Learning Infrastructure
Your training runs should fail for research reasons, not DevOps ones.
Distributed training infrastructure for embodied AI — reinforcement learning and vision-language-action models — provisioned on rented GPUs, kept running, and monitored, so your researchers do research.
Robot-Learning Infrastructure
- Typical length
- 3–6 weeks
- What you get
- 5 written deliverables, listed below
- What decides the number
- Cluster size, and how many frameworks and simulators you run.
- Scoping
- One call, then a written scope. We say no when it is not ours.
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01 The terms
Quoted per project, after a call. We do not publish a band for this, because a number without a scope is a guess and you would have to unpick it later anyway.
Tell us what the work has to do and when it has to be done, and you get the scope and the number in writing — or a straight answer that this is not ours.
03 The problem
Robot-learning frameworks are research code. They assume a cluster configured exactly like the authors' cluster, and they break in ways that eat a week of a researcher's time before a single useful run.
The GPUs are rented by the hour whether the run works or not.
04 What you get
- Environment build for your framework and simulators, reproducible
- Distributed training across multi-GPU rented instances
- Launch-path and configuration fixes, maintained on a branch you own
- Monitoring across runs, so a dead run is noticed in minutes
- Cost controls so idle GPUs are not billed overnight
05 How it runs
- Week 1
- Your framework, simulators and target hardware.
- Weeks 2–4
- Environment built, first distributed run reproduced.
- Weeks 5–6
- Monitoring, cost controls, handover or ongoing operation.
06 Whether this is for you
This is for you if
- Robotics startups and university labs training embodied policies
- Teams running open-source RL or VLA frameworks on rented GPUs
- Researchers losing weeks to infrastructure
This is not for you if
- Model research itself — we run the infrastructure, you own the science
- Large-language-model pretraining at frontier scale
- Physical robot hardware integration — that is a separate engagement
07 Proof
Our founder provisioned and ran distributed training for RLinf, an open-source reinforcement-learning framework for vision-language-action models, on rented multi-GPU instances: environment build, launch-path and config fixes on a dedicated branch, and monitoring across runs. It is one framework, run as our own work rather than for a client.
08 Questions
Which clouds?
Your own cloud account by default, so your data and checkpoints never leave it. If you have no preference we will suggest where the GPUs are cheapest.
Next 3–6 weeks
Robot-Learning Infrastructure
Tell us what the work has to do and when it has to be done. You will get a person who has read it, not a sequence.