Robot skill creation engine. From the first skill to the billionth.
Collect teleoperation demonstrations, curate and version your manipulation datasets, train imitation and reinforcement learning policies, then deploy and monitor them on real robots, end to end, in one platform.
estimated live totals across the platform
One loop, from demonstration to deployed policy.
Every stage of robot skill training in one system, so your data, your policies, and your fleet stay connected instead of scattered across scripts and buckets.
Collect
Capture demonstrations from any setup: teleoperation, kinesthetic teaching, VR rigs, or teleoperation gloves, logged with synchronized video, proprioception, and actions.
Curate
Search, filter, tag, and version your manipulation datasets. Catch quality issues before they waste a training run.
Train
Launch imitation or reinforcement learning runs. Compare policy versions and know exactly what data changed the result.
Deploy
Push policies to robots in production. Monitor success rates, trace failures back to the data, and evaluate new models as they arrive.
The platform at every stage.
Teleoperate and stream every signal, live.
Capture demonstrations from any rig with one line of Python. Every joint angle, camera frame, and spoken instruction lands in the cloud, synchronized and monitored in real time.
From pip install to a trained policy.
One log_* call for each of 14 first-class modalities, timestamped and synchronized the moment it lands. Stop the recording and the episode uploads itself, ready to train.
import neuracore as nc, time
nc.login()
nc.connect_robot(robot_name="Mujoco VX300s", urdf_path=URDF_PATH)
nc.create_dataset(name="Cube Handover")
nc.start_recording() # begin an episode
t = time.time()
nc.log_joint_positions(positions=obs.qpos, timestamp=t)
nc.log_rgb("wrist_cam", obs.rgb, timestamp=t)
nc.log_language(name="instruction",
language="Pick up the cube", timestamp=t)
nc.stop_recording() # auto-uploads to the cloudInfrastructure for both imitation and reinforcement learning.
Start from human demonstrations, then close the last mile with reinforcement learning. Neuracore runs both on the same datasets and versions every result.
Turn demonstrations into policies.
Train ACT, Diffusion Policy, π0, and vision-language-action models directly on your teleoperated datasets. Version every run and compare success rates side by side.
IL vs. RL, explained
Close the gap the demos can’t.
Fine-tune manipulation policies with reinforcement learning in simulation and on hardware, mixing simulated and real-world rollouts to push success rates past what imitation alone reaches.
Sim-to-real, in practiceThe build-it-yourself math never adds up.
Buy the layer, don't rebuild it.
Standing up data infra, training, and a deploy loop in-house costs a multi-million-dollar ML team and a year. Neuracore is that layer, ready the day you start.
See the mathWhere open source stops.
LeRobot is great for a laptop experiment. A managed platform is what turns scattered scripts and buckets into a versioned pipeline your whole team can run.
CompareRuns with or without ROS.
Multi-node collection and real-time control work out of the box, whether or not you run ROS. No middleware rewrite to adopt the platform.
How it worksRuns where your robots run.
Production robotics data doesn’t always get to leave the building. Neuracore deploys wherever your security team says it should.
Our cloud, your VPC, or fully on-prem.
Start on managed cloud, move to your own infrastructure when policy requires it. Same platform, same SDK, no migration rewrite.
Your demonstrations stay yours.
Datasets and trained policies belong to you, exportable at any time through the open-source SDK. No lock-in by data gravity.
Built for teams, not laptops.
Organizations, per-member roles, and scoped API keys keep collection rigs, training, and production robots separated cleanly.
Built for teams like yours.
Robotics OEMs & product teams
You ship robots that need learned skills in the product. Keep your engineers on hardware and application logic while Neuracore runs the data-to-policy loop underneath.
Tour the platformFull-stack & vertical robotics teams
Every new deployment means new tasks to learn, without a dedicated ML infrastructure team. Go from a new task to a deployed policy in days, not quarters.
Why buying beats buildingResearch labs & advanced R&D
You've outgrown laptop-scale experiments. Turn scattered scripts and buckets into versioned datasets and reproducible training runs your whole lab can share.
Compare with LeRobotLatest from Neuracore.
What is a VLA model?
Vision-language-action models, explained from the ground up: what they are, why they matter, and how to train one on your own robot.
Read the articleThe full loop, end to end.
Collect demonstrations, curate a dataset, train a policy, and deploy it to a real robot in one walkthrough.
Watch the demoPodcasts & talks.
Our team on VLA models, imitation and reinforcement learning, and where learned manipulation is heading in industry.
Listen in