---
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`