VShirokun/office-rover-miss-v21
office-rover-miss-v21 LeRobot v2.1 dataset for a single SO-101 arm: "pick up the red / green cube and put it in the box" with both cubes on the table. Two language-conditioned tasks (instructions in Russian). Built for fine-tuning NVIDIA Isaac GR00T N1.7 on one 24 GB RTX 4090 — recipe, patches and 1800 evaluated attempts: https://github.com/VShirokun/gr00t-on-4090 What is in it: random cube yaw (0–90°) and 30 % deliberately failed grasps followed by scripted recovery — the data… See the full description on the dataset page: https://huggingface.co/datasets/VShirokun/office-rover-miss-v21.
office-rover-miss-v21
LeRobot v2.1 dataset for a single SO-101 arm: "pick up the red / green cube and put it in the box" with both cubes on the table. Two language-conditioned tasks (instructions in Russian). Built for fine-tuning NVIDIA Isaac GR00T N1.7 on one 24 GB RTX 4090 — recipe, patches and 1800 evaluated attempts: https://github.com/VShirokun/gr00t-on-4090
What is in it: random cube yaw (0–90°) and 30 % deliberately failed grasps followed by scripted recovery — the data that taught the policy to retry. One half of the 92.17 % record mix.
How it was made — read before you trust it. Simulation only: MuJoCo with the official SO-101 model from mujoco_menagerie. Demonstrations come from a scripted operator (inverse kinematics), not a human teleoperator. Cube positions are random over a 12×30 cm zone; success is judged by physics (named cube in the box, other cube untouched). The policy never sees cube coordinates. Real lighting, glare, friction and servo backlash are not represented — the sim-to-real gap is unmeasured.
Loads directly: LeRobotDataset("VShirokun/office-rover-miss-v21"). Converted from LeRobot v3.0 with the upstream scripts/lerobot_conversion/convert_v3_to_v2.py.
License: CC BY 4.0. Please cite the repository above.