This dataset was created using LeRobot.
SAVRN Model Hub · Datasets by Task
Robotics Datasets
120 open-weight robotics datasets in the SAVRN Model Hub, with National Engineering Research Center for Big Data Software, IPEC at Shanghai AI Laboratory and Michal Podolinsky publishing the most.
120Datasets
83Publishers
8Licenses
SAVRN's Take
Robotics data is the scarce input of physical AI, and this page shows who is filling the gap. NVIDIA's GR00T simulation trajectories lead in downloads, followed by Genrobot's collection of more than 13,000 hours of real two-handed manipulation, and a long list of classic datasets such as DROID, Language Table and KUKA converted to the LeRobot format.
Most of these sets are Apache 2.0 or CC-BY-4.0, which makes them usable for commercial training with attribution. The practical constraint is size: real-world robot data runs to hundreds of gigabytes or more, so plan storage and bandwidth before you plan training runs. Mixing public data with demonstrations from your own robot remains the most reliable recipe.
Most Downloaded
| Dataset | Publisher | License | Monthly downloads |
|---|---|---|---|
| PhysicalAI-Robotics-GR00T-X-Embodiment-Sim | NVIDIA | cc-by-4.0 | 1.3M |
| 10Kh-RealOmin-OpenData | Genrobot.ai | cc-by-sa-4.0 | 689.4k |
| language_table_lerobot | IPEC at Shanghai AI Laboratory | apache-2.0 | 474.3k |
| droid_lerobot | IPEC at Shanghai AI Laboratory | apache-2.0 | 361.9k |
| droid_1.0.1 | Remi Cadene | apache-2.0 | 352.1k |
| kuka_lerobot | IPEC at Shanghai AI Laboratory | apache-2.0 | 316.5k |
| ABC-130k | XDOF | apache-2.0 | 296.4k |
| AgiBotWorld2026 | AgiBot World | cc-by-nc-sa-4.0 | 245.9k |
| stereo-550 | FPV Labs | other | 242.8k |
| ACE-Data-0 | ACE Robotics | other | 242.5k |
Licenses
| License | Datasets | Commercial use |
|---|---|---|
| apache-2.0 | 68 | Yes |
| not stated | 13 | Not stated |
| other | 12 | Read the license |
| cc-by-4.0 | 10 | Yes |
| cc-by-nc-4.0 | 8 | Not without separate permission |
| mit | 3 | Yes |
| cc-by-sa-4.0 | 3 | Yes |
| unknown | 1 | Read the license |
Who Publishes Them
All 120 Datasets, Page 2 of 2
This dataset was created using LeRobot.
This dataset was created using LeRobot.
Episodes 0 to 9 of DreamMachines/actuatorunboxingfullboxcropDall (revision 6a8bded63372), with the street seen behind the robot cell replaced by a measuring lab. The robot, table, box, tray and actuators are the original pixels. Actions, joint states, timestamps, task and the intervention columns are copied unchanged. Only the three camera videos differ. - 10 episodes, 5,308 frames at 50 fps Same ten episodes with other backgrounds: - kiteml/dm-actuator-unboxing-bg-aircraft-workshop: an aircraft workshop - kiteml/dm-actuator-unboxing-bg-hangar: an aircraft hangar 1. SAM 3 finds the background (street, buildings, sky, cars, passers-by) and the foreground (robot, gripper, box, tray, actuator…
This dataset was created using LeRobot.
This dataset was created using LeRobot.
This dataset was created using LeRobot.
Apache TsFile version of nduque/evalrobustnesse93fullfklocal240k. This is an evaluation-robustness teleoperation dataset recorded on a Koch robot arm and packaged in the LeRobot v2.1 layout. Each row is one control step: a 6-dimensional joint/gripper state, the 6-dimensional commanded action, and a 4-dimensional local end-effector pose. The source dataset card does not name the manipulation task; the metadata records a single task (taskindex = 0). - Time (INT64, milliseconds) — round(timestamp 1000); restarts at 0 for each episode. - episodeindex (TAG) — episode / device dimension. - taskindex (TAG) — task dimension (constant 0 here). - frameindex (FIELD, INT64) — source frame counter…
Exact data, public pretrained initialization, and offline Linux x8664 environment for 12 independent RMBench tasks. This snapshot preserves the rmbenchfastwamgtcovercurrentgbs64200k training settings: seed 42, global batch 64, 200000 optimizer steps, current observations, ground-truth subtask text, 32 actions, and subtask-boundary terminal hold. 600 demonstrations / 416711 frames, with the exact original LeRobot episode order and 46 cached GT subtask prompts. Public Wan 2.2, VAE, T5 and ActionDiT initialization are included. Previously trained project checkpoints are excluded. Repository-relative paths match the accompanying code: https://github.com/exploreing2333/fastwam-rmbench-training…
This dataset was created using LeRobot.
Gen-HumanWholeBody is a high-precision whole-body manipulation dataset captured from DAS-Ego, released by GenRobot.AI. It contains 600 hours of continuous whole-body human manipulation data reconstructed via GenRobot's proprietary Data Foundation Model (DFM), with an average joint position error of approximately 3 cm. The dataset covers 16,976 real-world scenarios and 1,281 distinct human skills, including tasks that require not only two-handed manipulation but also locomotion, body posture adjustment, and whole-body coordination. By sharing this dataset, GenRobot aims to provide Physical AI researchers with training-ready whole-body manipulation data and to catalyze a scalable data…
This dataset was created using LeRobot.
Image-in, trajectory-out navigation scenarios with 3D ground truth, generated in simulation with Habitat-Sim in the houses of HSSD (Habitat Synthetic Scenes Dataset, Khanna et al. 2023). Same schema and evaluator as the real-robot conversions JRDB); the CODa card documents every column, the coordinate frame, the point and map formats and the evaluation protocol. This set is the large training counterpart of those small real-world test sets. Code: - Input, the past. Rendered forward-camera images (640 x 480, 90° horizontal field of view, 1.2 m above the floor) of the 10 past steps and the current step, the agent's own past positions, and the - Ground truth, the future. The shortest navmesh…
The raw capture (murobotics/handumi-towel-raw-562) retargeted by inverse kinematics to bimanual AgileX PiPER: 14-D joint states and actions (radians, grippers in [0, 1]) with the wrist views resized to 640×480. 562 episodes, 200,835 frames. This is the training set of murobotics/handumi-towel-act-562. Recorded with HandUMI, a hand-worn bimanual interface whose swappable fingertips match the robot's installed parallel-jaw gripper. Task: grasp two corners of a flat yellow towel and fold it in half. 562 episodes, 30 fps, 111.9 minutes, no robot-collected data. Created with LeRobot (v3 format). Collected with the NONHUMAN community. Originally published as…
Kinex Benchmark Unified media and evidence storage for the Kinex Benchmark Space. The current index compares Codex + GPT-6 Astra / high and Kinex + GPT-6 Astra / high, with 84 selected seed-0 episodes per combination. Claude Code + Opus 5.5 / high and Kinex + Opus 5.5 / high are planned and have no results yet. Pending tasks remain pending; native verdicts, measured instructions, budgets and token counters are unchanged. Location data.json Current 164-task
is a large-scale, fully synthetic, ground-truth-complete benchmark for the two classical sensor-calibration problems of robotics, hand-eye calibration, A X = X B, and simultaneous robot-world / hand-eye calibration, A X = Y B, together with their spatio-temporal extension (unknown clock offset between the robot and the camera). Instead of images, every sequence consists of two time-stamped pose streams, the robot flange trajectory (from forward kinematics of real manipulator models) and the pose of the observed reference in the camera, both as exact ground truth and as measurements corrupted by physically motivated noise models. This isolates the numerical calibration problem from image…
LeRobot v2.1 dataset of LIBERO-10 demonstrations perturbed along the end-effector position x axis at scale 0.2 (alpha=0.2), with sequential square-wave injection (half-period = 1).
Apache TsFile version of binhng/liberoobjectmaskdepthIPECCOMMUNITYformat. A Franka robot dataset for the LIBERO-Object manipulation suite, distributed in the IPEC community format with object-mask and depth observations. Each of the ten tasks asks the robot to pick up a household object and place it in the basket. The converted time series records the robot's end-effector and joint state together with the commanded action at every control step. Tasks (taskindex): 0 pick up the salad dressing and place it in the basket; 1 bbq sauce; 2 ketchup; 3 tomato sauce; 4 alphabet soup; 5 cream cheese; 6 chocolate pudding; 7 butter; 8 orange juice; 9 milk. - Time (INT64, milliseconds) - per-episode…
LIBERO-Pro2.0 provides robot manipulation demonstrations for imitation learning and vision-language-action research. It contains 40,470 stored demonstrations across 32 base tasks in four LIBERO suites, including base tasks and perturbation variants. Each demonstration includes a language instruction, scene and wrist camera observations, robot states, and actions. Trajectories are generated by a privileged-state oracle and distributed in native HDF5 format. - 8,258,504 steps, with two 128 × 128 RGB camera views and 7-dimensional actions. - 6,745 review videos: five successful examples per task/variation instance. - Training data only; no validation or test split is provided. Repeated…
This dataset holds the starting state of a scene restoration task: compared with the target state, 1-3 objects in each scene have been moved to random positions and need to be put back. source.scene on each object identifies its scene. - position is the model origin (not the bounding-box center), in centimeters - size is the object's target extent; the model is already scaled to match it. (x, y) are the horizontal extents and z is the height. A model's own local up-axis may not align with z, so orient it using size as the reference - rotation is the complete absolute orientation as a quaternion in [x, y, z, w] order. Apply it directly, per object, and do not compose any additional rotation…
This dataset holds the target state of a scene restoration task: each scene should be restored to the arrangement given here. source.scene on each object identifies its scene and can be used to pair with other data from the same batch. The dataset is self-contained: every referenced model lives under assets/, there are no absolute paths, and no external asset library is required. - position is the model origin (not the bounding-box center), in centimeters - size is the object's target extent; the model is already scaled to match it. (x, y) are the horizontal extents and z is the height. A model's own local up-axis may not align with z, so orient it using size as the reference - rotation is…
This dataset was created using LeRobot.
LeRobot v2.1, 30 FPS, 117785 frames, 252 episodes: Task 784 (101), Task 789 (100), Task 809 (51). All four RGB streams and all 22 recorded robot state/action coordinates are retained. GR00T's intended policy uses the right arm and gripper (8 dimensions) plus external relative geometry (14 dimensions), with action horizon 24. Left-arm, head and lift states remain available for offline kinematic reconstruction. This is a staging dataset, not a validated metric-geometry training dataset. The 14-dimensional geometry slots are currently zero placeholders with valid=0 on every frame. annotation.releaselabelvalid is false on every frame. The zero values do not mean perfect alignment. Do not train…
This dataset was created using LeRobot. Dataset Structure "codebaseversion": "v3.0", "fps": 30, "features": { "action": { "dtype": "float32", "names": [ "shoulderpan.pos", "shoulderlift.pos", "elbowflex.pos", "wristflex.pos", "wristroll.pos", "gripper.pos" "shape": [ 6
Apache TsFile version of argus-systems/pickup-carrot-remove-parquet-metadata-2. A Trossen AI stationary-arm dataset for a single pick-and-place task: pick up a carrot and put it in the bowl. Each frame records the 14-D dual-arm action and 14-D observation state. Task (taskindex 0): "Pick up carrot and put it in the bowl". Note: the source meta/info.json and meta/episodes.jsonl declare 61 episodes / 27,261 frames, but the source repository only contains 58 data/chunk-000/episode.parquet files (episodes 0, 39 and 50 are absent); the 58 present episodes (25,922 frames) were converted. - Time (INT64, milliseconds) - per-episode sample time, round(timestamp 1000) in milliseconds (the source…
This dataset was created using LeRobot.
This dataset was created using LeRobot.
Apache TsFile version of Shiki42/PutCab-AB-RandomDelta-Train50. A timing-balanced 50-episode subset of RoboTwin PutCab-AB demonstrations, recorded on a simulated ALOHA bimanual robot. Selection is conditioned on the normalized reference offset uref = (delta + TBref) / (TAref + TBref) (where delta is right/B start minus left/A start): ten intervals 0,.1)... [.9,1) each contribute five trajectories, giving 25 negative and 25 positive deltas. Reused trajectories keep independent active-arm v3 timing; the new V2 trajectories add recorded feedback waits (safe-waypoint lift, drawer-readiness re-check). observation.state holds 14 joint/gripper drive targets and action is the next observation…
This dataset was created using LeRobot.
Apache TsFile version of taku-y/record-20260429-2. A SO-101 follower dataset for the task "Grab the toy 2". Each frame records the 6-D joint state and the 6-D commanded action. Task (taskindex 0): "Grab the toy 2". - Time (INT64, milliseconds) - per-episode sample time, round(timestamp 1000) in milliseconds (the source timestamp column equals Time / 1000 seconds and is therefore not stored separately). - episodeindex, taskindex (TAG) - device dimensions identifying one demonstration episode and one task; query a single episode with WHERE episodeindex=0. - action0..5, observationstate0..5 (FIELD) - FLOAT 6-D SO-101 joint action and 6-D observation state. - frameindex, sampleindex (INT64…
Video release for RobotEQ. Access is gated: you must request permission and wait for manual approval before downloading any files. This repository is public so the dataset page can be found and cited, but video files are not downloadable until the authors accept your request. You must be logged in (hf auth login) with an account that has been approved. CC BY-NC 4.0. Non-commercial research use only.
This dataset contains 300 simulated expert episodes across six RoboTwin tasks, with 15,455 windows for predicting the motion of object and robot end-effector points. It was prepared for the Trace-MoT experiments. The data is simulation-only. The object queries use simulator segmentation to identify the intended object; this provides oracle target information. - raw/ /episode /episode.hdf5: head, left, and right camera RGB observations, head depth and segmentation, camera calibration, robot end pose, and joint actions. - raw/ /episode /queries.npz and poses.npz: original point queries and simulator pose data used in label generation. - raw/ /episode /instruction.json: task instructions.…
This dataset was created using LeRobot.
This dataset was created using LeRobot.
Apache TsFile version of justintiensmith/rolloutpi05ReasoningStep076596Counting. A SO follower rollout dataset from a pi0.5 policy on a counting task family. The 32 language instructions ask the robot to move objects (a blue pen, a green pencil sharpener, a yellow block and an airplane toy) into or out of a bowl so that the bowl ends up holding a requested number of objects. Each frame records the 6-D joint state and the 6-D commanded action. Tasks (taskindex 0-31) include instructions such as "Leave exactly one object in the bowl", "Adjust the bowl so that it contains two objects", "Leave exactly three objects in the bowl", "Adjust the bowl so that it contains zero objects", "Move the…
This dataset was created using LeRobot.
This dataset was created using LeRobot.
This dataset was created using LeRobot.
This dataset was created using LeRobot.
This dataset was created using LeRobot.
This dataset was created using LeRobot.
This dataset was created using LeRobot.
This dataset was created using LeRobot.
Apache TsFile version of nikodembartnik/so101pencilholder1. A SO follower dataset for the task "put the pen in the wooden box" (source pretty name: "Task: put pen in wooden box"). Each frame records the 6-D joint state and the 6-D commanded action. Task (taskindex 0): "put the pen in the wooden box". - Time (INT64, milliseconds) - per-episode sample time, round(timestamp 1000) in milliseconds (the source timestamp column equals Time / 1000 seconds and is therefore not stored separately). - episodeindex, taskindex (TAG) - device dimensions identifying one demonstration episode and one task; query a single episode with WHERE episodeindex=0. - action0..5, observationstate0..5 (FIELD) - FLOAT…
This dataset was created using LeRobot.
This dataset was created using LeRobot. SO-101 turntable pick-and-place (robot lilguy), September 2026. A single SO-101 follower arm, teleoperated from an SO-101 leader arm, picks a cube off a slowly spinning turntable and places it in a basket. Two cube colours (blue, green) and two operator speeds (slow, fast). Recorded with MakerMods Lab on 2026-09-27 and 2026-09-29. - observation.state: 6 follower joint positions - action: 6 leader joint positions - observation.images.head: Intel RealSense D435if RGB, 640×480, overhead view - observation.images.headdepth: RealSense depth aligned to the head RGB, 12-bit lossless HEVC (decode with lerobot's depth support to get millimetres; values under…
This dataset was created using LeRobot.
This dataset was created using LeRobot.
Teleoperated demonstration dataset for training a grasping policy on an SO-ARM101 arm with an AmazingHand dexterous hand (imitation learning). Pick up the cube with the dexterous hand — grasp a cube on the table using the dexterous hand. Collection flow: an operator moves the leader arm → the follower tracks it while its gripper proportionally drives the hand's open/close → joint angles and both camera streams are recorded synchronously. Per-episode flow: start from a fixed initial pose → perform one complete grasp (approach → grasp → lift → move → place) → end. Position coverage: the cube was placed at 4 different table positions, 5 episodes each, to teach positional generalization.…
This dataset was created using LeRobot.
This dataset was created using LeRobot.
544 teleoperated Franka episodes: 3 tactile sensors × 4 tasks, with wrist + third-person RGB, robot state, actions, and raw tactile readings. Train and val share a folder; the original split (482 / 62) is in valsplit.json. Raw tactile dimension D, per finger
LeRobot v3 dataset (albagon/telearmsdr3002looks): bimanual YAM teleoperation demos from the TeleArms game, re-rendered in MuJoCo with visual domain randomization for sim-to-real transfer of a pi0.5 policy. - Two independent DR looks per demo (dr00, dr01). The dr00 episodes are albagon/telearmsdr3001look. - 18 episodes whose wrist view was near-black for the whole episode are excluded (provenance/excludedarkwristtrain.json). - ckpt0's train/holdout split by demo UUID: provenance/ckpt0splitbyuuid.csv. The DR shares in the table below are for the whole render; the drreport summary covers all looks. Full config and per-factor ranges: provenance/drconfigresolved.json; render report…
This dataset was created using LeRobot.
This dataset was created using LeRobot.
This dataset was created using LeRobot.
This dataset was created using LeRobot.
This dataset was created using LeRobot. Part B (a white disc with a hub and a bar) is found with the wrist camera, picked from the plate and seated into fixture A. Universal Robots UR7e, Robotiq gripper, wrist-mounted Intel RealSense D405. Collected autonomously during a continuous 48-hour run that started on 2026-10-10, to cover the lighting changes of day and night. This repository is one part of that run: 101 episodes, 65387 frames at 30 fps. - State and action (16 values each): 6 joint angles, TCP position, TCP orientation (6D), gripper position. Also recorded: joint velocity, torque and current, TCP force, raw force/torque wrench, gripper effort, capture time. - The wrist camera is…
This dataset was created using LeRobot. Part B (a white disc with a hub and a bar) is found with the wrist camera, picked from the plate and seated into fixture A. Universal Robots UR7e, Robotiq gripper, wrist-mounted Intel RealSense D405. Collected autonomously during a continuous 48-hour run that started on 2026-10-10, to cover the lighting changes of day and night. This repository is one part of that run: 100 episodes, 64553 frames at 30 fps. - State and action (16 values each): 6 joint angles, TCP position, TCP orientation (6D), gripper position. Also recorded: joint velocity, torque and current, TCP force, raw force/torque wrench, gripper effort, capture time. - The wrist camera is…
Apache TsFile version of zwonzis/xarm7-pick-orange-ball-large. A UFactory xArm7 dataset for the task "pick up the orange ball and put it in the cyan bowl". Each frame records the 8-D arm state and the 8-D commanded action. Task (taskindex 0): "pick up the orange ball and put it in the cyan bowl". - Time (INT64, milliseconds) - per-episode sample time, round(timestamp 1000) in milliseconds (the source timestamp column equals Time / 1000 seconds and is therefore not stored separately). - episodeindex, taskindex (TAG) - device dimensions identifying one demonstration episode and one task; query a single episode with WHERE episodeindex=0. - observationstate0..7, action0..7 (FIELD) - FLOAT 8-D…