/ INTEGRACJE
Pasuje do stacku, którego już używasz.
Każdy zbiór danych trafia do narzędzi, które Twój zespół otwiera codziennie — frameworków treningowych, symulatorów, wizualizatorów i narzędzi do śledzenia eksperymentów, bez kodu łączącego do utrzymania.
/ Middleware and tooling
ROS 2
Record robot demonstrations from a live ROS 2 graph with rosbag2, avoid the QoS and clock pitfalls that corrupt recordings, and export to training formats.
Foxglove
Use Foxglove to visually inspect and QA robot demonstration recordings — live or from MCAP — with 3D, image, and plot panels built for robotics data.
/ Training and datasets
LeRobot
LeRobot is Hugging Face's robotics library. Here is how a Telemanual export maps onto LeRobotDataset, and how to load, train, and evaluate against it.
PyTorch
Turning a captured robot dataset into a PyTorch pipeline — Dataset choices, video decode bottlenecks, episode-safe splits, and reproducible sampling.
Hugging Face Hub
Host robot datasets on the Hugging Face Hub with dataset cards, access control, and pinned revisions — the layer between raw capture and a citable release.
/ Simulation
NVIDIA Isaac Lab
How NVIDIA Isaac Lab fits a real-data workflow — digital twins, HDF5 demonstration replay, sim-and-real co-training, and the limits of sim evaluation.
MuJoCo
MuJoCo is DeepMind's open-source physics engine for contact-rich robotics research. Here is how recorded demonstration data connects to MJCF models.
Wprowadź to w życie.
Powiedz nam, czego mają nauczyć się Twoje roboty. Dobierzemy stanowisko, operatorów, protokół i pierwsze zbiory danych — zwykle podczas jednej rozmowy.