---
pretty_name: RevoBench Refactor — Ten Accepted Multimodal Episodes
language:
- en
tags:
- robotics
- lerobot
- tactile
- simulation
---

# RevoBench-Refactor: ten-task reference batch

This batch contains **10 accepted simulated demonstrations, 29,170 frames at 60 Hz, and 140 synchronized videos** (486.17 seconds of robot time). Each task contributes one complete episode. The robot is the new RevoTron with textured, calibrated Revo3 **MT** hands and the approved palm-facing **180° wrist fisheye** cameras. The previous fingertip optical calibration is retained.

The format is **LeRobot v2.1**, validated with **LeRobot 0.3.3 / PyAV**. These are selected scripted reference executions, not a statistically measured success rate or a large training benchmark. The only split is `train`.

## Task previews

![Ten RevoBench task previews: hammer, charger, tray, pouring, cup, knob, pipette, peg, kitchen and drill.](media/ten_task_overview.jpg)

Overview-camera frames from the ten selected recordings, arranged in episode order. [View full resolution](media/ten_task_overview.jpg) · [Source frames](media/ten_task_overview_sources.json).

## Episodes

| Episode | Task | Frames | Seed | Randomized layout |
|---:|---|---:|---:|:---:|
| 0 | `hammer` | 1403 | 0 | no |
| 1 | `plug_charger` | 2457 | 1 | yes |
| 2 | `tray_carry` | 1676 | 1 | yes |
| 3 | `pour_can` | 3720 | 1 | yes |
| 4 | `cup_hang` | 3892 | 1 | yes |
| 5 | `rotate_knob` | 2230 | 1 | yes |
| 6 | `insert_pipette` | 2551 | 1 | yes |
| 7 | `insert_peg` | 3033 | 1 | yes |
| 8 | `kitchen` | 5424 | 1 | yes |
| 9 | `power_drill` | 2784 | 4 | yes |

English task instructions are in `meta/tasks.jsonl`. `meta/batch_provenance.json` maps every episode to its original recording. Episode, global-frame, task and tactile-target indices were assigned by the shared exporter; the original per-task datasets remain preserved separately.

## Recorded fields

Use `meta/info.json` for exact feature names, shapes and joint ordering.

| Fields | Shape per frame / interpretation |
|---|---|
| `observation.state`, `action` | `[56]`, measured joint positions and absolute joint commands in radians; observations precede the corresponding command |
| `observation.images.head`, `.wrist_left`, `.wrist_right`, `.overview` | Four RGB videos; the two wrist cameras use 180° fisheye projection |
| `observation.images.tactile_{side}_{finger}` | Ten optical tactile RGB videos, five per hand |
| `observation.tactile_depth.{side}_{finger}` | `[1,240,320]` numeric float32 optical overlap depth in metres |
| `observation.tactile_marker.{side}_{finger}` | `[100,3]` float32 marker displacement in DIP-local coordinates, metres |
| `observation.tactile_marker_valid.{side}_{finger}` | `[100]` fixed boolean calibration validity mask |
| `observation.tactile_pressure.{side}_{region}` | MT regional response vectors, **451 channels per hand** |
| `observation.tactile_tip_wrench.{side}_fingers` | `[5,6]` normal plus friction forces and moments about the DIP origin, in DIP-local coordinates; N and N m |
| `observation.tactile_target_id` | `[1]` dataset-wide target ID; resolve through `meta/tactile_targets.json` |

Sides are `left` and `right`; fingers are `thumb`, `index`, `middle`, `ring`, `pinky`. Pressure has thumb regions of 31+57 channels, four finger regions of 21+52 each, and 71 palm channels. It is a nonnegative response after column-normalized spatial diffusion, **not Pa or N**, and can exceed 1. Do not clip it to [0,1]. The tactile backend observes one explicitly identified target at a time, not all scene contacts. Per-episode `target_mapping.json` maps original raw target slots to dataset-wide IDs.

Gains are configuration, not action dimensions. Every task uses hand stiffness/damping 80/8, arm stiffness `[600,600,600,450,450,300,300]` and arm damping 30. Recording/control run at 60 Hz; physics runs at 480 Hz.

## Reading the data

Authenticate with an account permitted to read this private dataset (`hf auth login`). Use the pinned LeRobot 0.3.3 reader environment documented in the [code repository](https://github.com/DavidLXu/RevoBench-Refactor/tree/c2646255984794f755e391c18e58c745c63e198e). The snippet downloads a fixed format tag, then loads local files:

```python
from huggingface_hub import snapshot_download
from lerobot.datasets.lerobot_dataset import LeRobotDataset

repo_id = "DavidLXu/RevoBench-Refactor"
root = snapshot_download(repo_id, repo_type="dataset", revision="v2.1")
dataset = LeRobotDataset(
    repo_id,
    root=root,
    download_videos=False,
    video_backend="pyav",
    delta_timestamps={"action": [i / 60 for i in range(16)]},
)
sample = dataset[0]
print(sample["observation.state"].shape)  # torch.Size([56])
print(sample["action"].shape)             # torch.Size([16, 56])
```

For an immutable experiment, replace `v2.1` with this dataset's full Hub commit SHA. Preserve episode boundaries and use `action_is_pad` when training with action chunks. The official reader has been checked at the start, middle and end of all ten episodes, including repeated final-action padding.

## Evidence and scope

`meta/validation_summary.json` contains combined-dataset numeric and official-reader validation. The exporter decoded every video to check all frame counts and timestamps. `meta/episodes/episode_XXXXXX/` preserves the original raw manifest, physical report, calibration hashes and target mapping. `meta/acceptance/` contains the selected task acceptance, randomized-layout evidence and individual physical, motion and visual reviews. `meta/file_manifest.json` records payload sizes and SHA-256 hashes, excluding itself.

These recordings share optical model `physical_geometry_optical_intervals_v1_candidate`. Numeric depth represents the connected target overlap along a calibrated ray at its gel-surface endpoint; it is not measured normal silicone indentation. The tray uses its closed authored source surface rather than the cooked PhysX SDF grid. Real-sensor fidelity has not been established. In the selected tray episode, thumb contact is on the shell outside the calibrated gel, so zero thumb optical response is valid.

`pour_can` uses the versioned granular surrogate `pour_can_opened_granular_v2_cavity_frame`; its containment predicate remains provisional and does not assert that every particle reaches the bowl. `insert_pipette` ends with the inserted tube still held. `power_drill` demonstrates pickup, non-contact board alignment and upright replacement/release; it does not simulate spindle rotation or drilling.

Code and reproduction: [RevoBench-Refactor at c264625](https://github.com/DavidLXu/RevoBench-Refactor/tree/c2646255984794f755e391c18e58c745c63e198e), [recording instructions](https://github.com/DavidLXu/RevoBench-Refactor/blob/c2646255984794f755e391c18e58c745c63e198e/docs/REPRODUCE.md), [sensor contract](https://github.com/DavidLXu/RevoBench-Refactor/blob/c2646255984794f755e391c18e58c745c63e198e/docs/SENSOR_MODEL.md), [physical criteria](https://github.com/DavidLXu/RevoBench-Refactor/blob/c2646255984794f755e391c18e58c745c63e198e/docs/VALIDATION.md).

Robot, task assets, textures and calibration retain their respective upstream provenance; no new blanket asset license is asserted here. See [third-party notices](https://github.com/DavidLXu/RevoBench-Refactor/blob/c2646255984794f755e391c18e58c745c63e198e/docs/THIRD_PARTY.md). This upload includes recorded data and evidence, not the full simulation asset bundle.