Datasets:

DavidLXu
/

RevoBench-Refactor

---
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.

	---
	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.