---
license: mit
task_categories:
- robotics
tags:
- LeRobot
- d3il
- imitation-learning
- robot-learning
---

# D3IL Avoiding Vision 224

This is the original D3IL Avoiding demonstration dataset converted to the
LeRobot v2.1 format and augmented with state-aligned 224 x 224 RGB observations.

It contains all 96 original Avoiding demonstrations (7,305 training frames),
covering the benchmark's 24 avoidance modes with four demonstrations per mode.
The numeric demonstrations are preserved from the original D3IL pickle logs.

## Dataset summary

| Property | Value |
|---|---:|
| Episodes | 96 |
| Frames | 7,305 |
| FPS | 29 |
| Tasks | 1 |
| State dimension | 4 |
| Action dimension | 2 |
| Cameras | 2 |
| Image resolution | 224 x 224 |

The camera observations are available under:

- `observation.images.bp_cam`
- `observation.images.inhand_cam`

Load the public dataset directly with LeRobot:

```python
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("shivakanthsujit/d3il_avoiding_vision_224")
```

The images were produced by restoring every logged robot pose into the original
D3IL MuJoCo environment and rendering the benchmark cameras. Actions were not
replayed and physics was not advanced, so the images remain aligned with the
original recorded states.

`action` stores the original next absolute desired controller target. To train
with D3IL's native delta action, subtract the current desired target (the first
two values of `observation.state`) at the corresponding horizon step from
`action`.

The metadata also retains `source_file`, `source_episode_index`, and
`source_fps` for provenance. `state_is_pad`, `action_is_pad`, and
`camera_is_pad` are included for compatibility with the multi-task converter;
all features in this single-task dataset are valid and unpadded.

## Source

- D3IL repository: https://github.com/ALRhub/d3il
- Project page: https://alrhub.github.io/d3il-website/
- Paper: *Towards Diverse Behaviors: A Benchmark for Imitation Learning with
  Human Demonstrations*, ICLR 2024.

Please cite the original D3IL work when using this dataset.