mirror of
https://github.com/huggingface/lerobot.git
synced 2026-07-26 19:26:16 +00:00
Pepijn
0ac610e029
- Remove the "Related work" block referencing the prototype branch feat/robomme-integration; the PR stands on its own. - Point all dataset references at lerobot/robomme (docs, env module docstring, RoboMMEEnvConfig docstring) — this is the canonical HF location once the dataset is mirrored. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
115 lines
5.4 KiB
Plaintext
115 lines
5.4 KiB
Plaintext
# RoboMME Benchmark
|
||
|
||
[RoboMME](https://robomme.github.io) is a memory-augmented manipulation benchmark built on ManiSkill (SAPIEN). It evaluates a robot's ability to retain and use information across an episode — counting, object permanence, reference, and imitation.
|
||
|
||
- **16 tasks** across 4 memory-skill suites
|
||
- **1,600 training demos** (100 per task, 50 val, 50 test)
|
||
- **Dataset**: [`lerobot/robomme`](https://huggingface.co/datasets/lerobot/robomme) — LeRobot v3.0, 768K frames at 10 fps
|
||
- **Simulator**: ManiSkill / SAPIEN, Panda arm, Linux only
|
||
|
||
## Tasks
|
||
|
||
| Suite | Tasks |
|
||
| --------------------------------- | ------------------------------------------------------------- |
|
||
| **Counting** (temporal memory) | BinFill, PickXtimes, SwingXtimes, StopCube |
|
||
| **Permanence** (spatial memory) | VideoUnmask, VideoUnmaskSwap, ButtonUnmask, ButtonUnmaskSwap |
|
||
| **Reference** (object memory) | PickHighlight, VideoRepick, VideoPlaceButton, VideoPlaceOrder |
|
||
| **Imitation** (procedural memory) | MoveCube, InsertPeg, PatternLock, RouteStick |
|
||
|
||
## Installation
|
||
|
||
> RoboMME requires **Linux** (ManiSkill/SAPIEN uses Vulkan rendering). Docker is recommended to isolate dependency conflicts.
|
||
|
||
### Native (Linux)
|
||
|
||
```bash
|
||
pip install 'lerobot[robomme]'
|
||
```
|
||
|
||
> **Dependency note**: `robomme` pulls in `mani-skill` which pins `gymnasium==0.29.1` and `numpy<2.0.0`. These conflict with lerobot's default pins. On Linux you can pin both in your env; see the Docker approach below to avoid conflicts entirely.
|
||
|
||
### Docker (recommended)
|
||
|
||
```bash
|
||
# Build base image first (from repo root)
|
||
docker build -f docker/Dockerfile.eval-base -t lerobot-eval-base .
|
||
|
||
# Build RoboMME eval image (applies gymnasium + numpy pin overrides)
|
||
docker build -f docker/Dockerfile.eval-robomme -t lerobot-robomme .
|
||
```
|
||
|
||
The `Dockerfile.eval-robomme` overrides `gymnasium==0.29.1` and `numpy==1.26.4` after lerobot's install. Both versions are runtime-safe for lerobot's actual API usage.
|
||
|
||
## Running Evaluation
|
||
|
||
```bash
|
||
lerobot-eval \
|
||
--policy.path=<your_policy_repo> \
|
||
--env.type=robomme \
|
||
--env.task=PickXtimes \
|
||
--env.dataset_split=test \
|
||
--eval.batch_size=1 \
|
||
--eval.n_episodes=50
|
||
```
|
||
|
||
### Key CLI options for `env.type=robomme`
|
||
|
||
| Option | Default | Description |
|
||
| -------------------- | ------------- | -------------------------------------------------- |
|
||
| `env.task` | `PickXtimes` | Any of the 16 task names above |
|
||
| `env.dataset_split` | `test` | `train`, `val`, or `test` |
|
||
| `env.action_space` | `joint_angle` | `joint_angle` (8-D) or `ee_pose` (7-D) |
|
||
| `env.episode_length` | `300` | Max steps per episode |
|
||
| `env.task_ids` | `null` | List of episode indices to evaluate (null = `[0]`) |
|
||
|
||
## Dataset
|
||
|
||
The dataset [`lerobot/robomme`](https://huggingface.co/datasets/lerobot/robomme) is in **LeRobot v3.0 format** and can be loaded directly:
|
||
|
||
```python
|
||
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
|
||
|
||
dataset = LeRobotDataset("lerobot/robomme")
|
||
```
|
||
|
||
### Dataset features
|
||
|
||
| Feature | Shape | Description |
|
||
| ------------------ | ------------- | ------------------------------- |
|
||
| `image` | (256, 256, 3) | Front camera RGB |
|
||
| `wrist_image` | (256, 256, 3) | Wrist camera RGB |
|
||
| `actions` | (8,) | Joint angles + gripper |
|
||
| `state` | (8,) | Joint positions + gripper state |
|
||
| `simple_subgoal` | str | High-level language annotation |
|
||
| `grounded_subgoal` | str | Grounded language annotation |
|
||
| `episode_index` | int | Episode ID |
|
||
| `frame_index` | int | Frame within episode |
|
||
|
||
### Feature key alignment (training)
|
||
|
||
The env wrapper exposes `front_rgb` and `wrist_rgb` as observation keys. The `features_map` in `RoboMMEEnv` maps these to `obs_images.front` and `obs_images.wrist` for the policy.
|
||
|
||
The dataset uses `image` and `wrist_image` as column names. When fine-tuning from the dataset, you may need to rename columns to match your policy's expected input keys:
|
||
|
||
```python
|
||
dataset = LeRobotDataset("lerobot/robomme")
|
||
# rename dataset columns if needed:
|
||
# dataset.hf_dataset = dataset.hf_dataset.rename_column("image", "front_rgb")
|
||
```
|
||
|
||
## Action Spaces
|
||
|
||
| Type | Dim | Description |
|
||
| ------------- | --- | --------------------------------------------------------- |
|
||
| `joint_angle` | 8 | 7 joint angles + 1 gripper (−1 closed, +1 open, absolute) |
|
||
| `ee_pose` | 7 | xyz + roll/pitch/yaw + gripper |
|
||
|
||
Set via `--env.action_space=joint_angle` (default) or `--env.action_space=ee_pose`.
|
||
|
||
## Platform Notes
|
||
|
||
- **Linux only**: ManiSkill requires SAPIEN/Vulkan. macOS and Windows are not supported.
|
||
- **GPU recommended**: Rendering is CPU-capable but slow; CUDA + Vulkan gives full speed.
|
||
- **gymnasium / numpy conflict**: See installation note above. Docker image handles this automatically.
|
||
- **ManiSkill fork**: `robomme` depends on a specific ManiSkill fork (`YinpeiDai/ManiSkill`), pulled in automatically via the `robomme` package.
|