| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| tags: |
| - robotics |
| - vla |
| - robosuite |
| - imitation-learning |
| - flock |
| dataset_info: |
| features: |
| - name: episode_index |
| dtype: int32 |
| - name: step_index |
| dtype: int32 |
| - name: task |
| dtype: string |
| - name: difficulty |
| dtype: string |
| - name: instruction |
| dtype: string |
| - name: image |
| dtype: image |
| - name: action |
| sequence: float32 |
| - name: proprio |
| sequence: float32 |
| - name: reward |
| dtype: float32 |
| - name: done |
| dtype: bool |
| splits: |
| - name: train |
| num_examples: 25328 |
| --- |
| |
| # FLock Robotics VLA Training Dataset v2 |
|
|
| Expert demonstrations for the FLock Robotics VLA competition task. All trajectories are successful. |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("random-sequence/flock-robotics-vla-training-v2") |
| print(ds["train"][0]) |
| # Keys: episode_index, step_index, task, difficulty, instruction, |
| # image (PIL), action [7], proprio [25], reward, done |
| ``` |
|
|
| ## Dataset statistics |
|
|
| | Task | Episodes | Difficulty | Steps | |
| |---|---|---|---| |
| | lift_cube | 8 | low | ~1,100 | |
| | pick_place_can | 20 | low | ~3,800 | |
| | pick_place_milk | 19 | low | ~3,600 | |
| | pick_place_bread | 18 | low | ~3,300 | |
| | pick_place_cereal | 16 | low | ~2,900 | |
| | stack_blocks | 22 | medium | ~10,600 | |
| | **Total** | **103** | | **25,328** | |
|
|
| All 103 episodes are 100% successful (success-filtered collection: each episode was retried until success). |
|
|
| ## Schema |
|
|
| Each row is one timestep: |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `episode_index` | int32 | Trajectory ID (0–102) | |
| | `step_index` | int32 | Timestep within episode | |
| | `task` | string | Task name (e.g. `lift_cube`) | |
| | `difficulty` | string | `low` or `medium` | |
| | `instruction` | string | Natural language task instruction | |
| | `image` | Image | 224×224 RGB agent-view frame | |
| | `action` | float32[7] | OSC delta `[Δx, Δy, Δz, Δroll, Δpitch, Δyaw, gripper]` clipped to [-1, 1] | |
| | `proprio` | float32[25] | Robot state: joint pos/vel, EEF pose, gripper | |
| | `reward` | float32 | Sparse reward (1.0 at success, 0 otherwise) | |
| | `done` | bool | Episode termination flag | |
|
|
| Schema version: `robotics_vla_sample_trajectory_v1` |
|
|
| ## Environment |
|
|
| - Simulator: robosuite 1.5.2 |
| - Robot: Panda 7-DOF |
| - Controller: BASIC composite (OSC_POSE arm + gripper) |
| - Camera: agentview 224×224 |
| |
| ## Raw zip |
| |
| `training_traces_v2.zip` is also available for backward compatibility. It contains the same data as individual `metadata.json` + `trajectory.npz` pairs under `trajectories/traj_NNN_<task>_<difficulty>/`. |
|
|
| SHA256: `e255a52aa0bb9fbf77a6d44ce368e41cb1d2dec184c2e7b6530e83088d772411` |
|
|
