---
pretty_name: UF850 VR Teleoperation Hang The Cup RLDS 10 Hz No-Op Filtered
language:
  - en
tags:
  - robotics
  - imitation-learning
  - teleoperation
  - uf850
  - quest-vr
  - realsense
  - rlds
  - tensorflow-datasets
  - openvla
  - 10hz
  - noop-filtered
size_categories:
  - 1K<n<10K
---

# UF850 VR Teleoperation Hang The Cup RLDS 10 Hz No-Op Filtered

Public OpenVLA-OFT/VLA-Adapter-compatible RLDS/TFDS trajectories derived from
[`CASE-Lab/uf850-vr-teleop-hang-the-cup-raw`](https://huggingface.co/datasets/CASE-Lab/uf850-vr-teleop-hang-the-cup-raw).
The raw release is endpoint-resampled to a local, checksum-pinned 10 Hz
intermediate and then filtered for idle actions. The final published release
keeps the original 10 Hz endpoint action semantics.

## Task

Hang the yellow cup on the cup rack.

Instructions stored in `language_instruction`:

- `hang the yellow cup on the cup rack`

## Dataset summary

| Item | Value |
| --- | ---: |
| Episodes | 30 |
| Source 10 Hz transition steps | 5,953 |
| Retained RLDS steps | 4,256 |
| Removed no-op steps | 1,697 |
| Retained fraction | 71.49% |
| Split | `train` only |
| TFDS version | `1.0.1` |

## No-op filtering and semantics

A 10 Hz step is removed only when translation is below
`0.02 cm`, wrapped rotation is below
`0.002 rad`, and the absolute gripper state is unchanged.
Gripper transitions are always retained. Actions keep their original 10 Hz
endpoint meaning and are not recomputed after filtering. Encoded source JPEGs
are copied byte-for-byte into the filtered TFRecords.

- `observation/image`: external RGB, 224 x 224.
- `observation/hand_image`: wrist RGB, 224 x 224.
- `observation/end_effector_pose`: six UF850 joint angles in radians.
- `action[0:3]`: relative TCP XYZ in centimetres.
- `action[3:6]`: wrapped relative Euler rotation in radians.
- `action[6]`: absolute gripper target (`-1` open, `+1` closed).

## Loading

```python
from huggingface_hub import snapshot_download
import tensorflow_datasets as tfds

root = snapshot_download(repo_id="CASE-Lab/uf850-vr-teleop-hang-the-cup-rlds-noop-filtered", repo_type="dataset")
builder = tfds.builder_from_directory(f"{root}/utokyo_xarm_pick_and_place_converted_externally_to_rlds/1.0.1")
dataset = builder.as_dataset(split="train", shuffle_files=False)
```

Use the repository root as VLA-Adapter `data_root_dir`. UF850 task repositories
share the same internal TFDS dataset name and must remain in separate local
roots.

## Provenance and limitations

`SOURCE_README.md` preserves the superseded source card,
`conversion_manifest.json` records per-episode filtering and source hashes,
and `SHA256SUMS` covers every published payload. Only a train split is
provided; there are no independent per-episode success labels.