huggingface--lerobot
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131 行
6.1 KiB
Plaintext
131 行
6.1 KiB
Plaintext
# RoboMME
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[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.
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- **16 tasks** across 4 memory-skill suites
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- **1,600 training demos** (100 per task, 50 val, 50 test)
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- **Dataset**: [`lerobot/robomme`](https://huggingface.co/datasets/lerobot/robomme) — LeRobot v3.0, 768K frames at 10 fps
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- **Simulator**: ManiSkill / SAPIEN, Panda arm, Linux only
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## Tasks
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| Suite | Tasks |
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| --------------------------------- | ------------------------------------------------------------- |
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| **Counting** (temporal memory) | BinFill, PickXtimes, SwingXtimes, StopCube |
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| **Permanence** (spatial memory) | VideoUnmask, VideoUnmaskSwap, ButtonUnmask, ButtonUnmaskSwap |
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| **Reference** (object memory) | PickHighlight, VideoRepick, VideoPlaceButton, VideoPlaceOrder |
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| **Imitation** (procedural memory) | MoveCube, InsertPeg, PatternLock, RouteStick |
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## Installation
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> RoboMME requires **Linux** (ManiSkill/SAPIEN uses Vulkan rendering). Docker is recommended to isolate dependency conflicts.
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### Native (Linux)
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```bash
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pip install --override <(printf 'gymnasium==0.29.1\nnumpy==1.26.4\n') \
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-e '.[smolvla,av-dep]' \
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'robomme @ git+https://github.com/RoboMME/robomme_benchmark.git@main'
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```
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> **Dependency note**: `mani-skill` (pulled by `robomme`) pins `gymnasium==0.29.1` and `numpy<2.0.0`, which conflict with lerobot's base `numpy>=2.0.0`. That's why `robomme` is not a pyproject extra — use the override install above, or the Docker approach below to avoid conflicts entirely.
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### Docker (recommended)
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```bash
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# Build base image first (from repo root)
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docker build -f docker/Dockerfile.eval-base -t lerobot-eval-base .
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# Build RoboMME eval image (applies gymnasium + numpy pin overrides)
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docker build -f docker/Dockerfile.benchmark.robomme -t lerobot-robomme .
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```
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The `docker/Dockerfile.benchmark.robomme` image 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.
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## Running Evaluation
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### Default (single task, single episode)
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```bash
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lerobot-eval \
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--policy.path=<your_policy_repo> \
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--env.type=robomme \
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--env.task=PickXtimes \
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--env.dataset_split=test \
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--env.task_ids=[0] \
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--eval.batch_size=1 \
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--eval.n_episodes=1
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```
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### Multi-task evaluation
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Evaluate multiple tasks in one run by comma-separating task names. Use `task_ids` to control which episodes are evaluated per task. Recommended: 50 episodes per task for the test split.
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```bash
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lerobot-eval \
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--policy.path=<your_policy_repo> \
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--env.type=robomme \
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--env.task=PickXtimes,BinFill,StopCube,MoveCube,InsertPeg \
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--env.dataset_split=test \
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--env.task_ids=[0,1,2,3,4,5,6,7,8,9] \
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--eval.batch_size=1 \
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--eval.n_episodes=50
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```
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### Key CLI options for `env.type=robomme`
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| Option | Default | Description |
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| -------------------- | ------------- | -------------------------------------------------- |
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| `env.task` | `PickXtimes` | Any of the 16 task names above (comma-separated) |
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| `env.dataset_split` | `test` | `train`, `val`, or `test` |
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| `env.action_space` | `joint_angle` | `joint_angle` (8-D) or `ee_pose` (7-D) |
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| `env.episode_length` | `300` | Max steps per episode |
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| `env.task_ids` | `null` | List of episode indices to evaluate (null = `[0]`) |
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## Dataset
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The dataset [`lerobot/robomme`](https://huggingface.co/datasets/lerobot/robomme) is in **LeRobot v3.0 format** and can be loaded directly:
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```python
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from lerobot.datasets.lerobot_dataset import LeRobotDataset
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dataset = LeRobotDataset("lerobot/robomme")
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```
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### Dataset features
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| Feature | Shape | Description |
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| ------------------ | ------------- | ------------------------------- |
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| `image` | (256, 256, 3) | Front camera RGB |
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| `wrist_image` | (256, 256, 3) | Wrist camera RGB |
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| `actions` | (8,) | Joint angles + gripper |
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| `state` | (8,) | Joint positions + gripper state |
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| `simple_subgoal` | str | High-level language annotation |
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| `grounded_subgoal` | str | Grounded language annotation |
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| `episode_index` | int | Episode ID |
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| `frame_index` | int | Frame within episode |
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### Feature key alignment (training)
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The env wrapper exposes `pixels/image` and `pixels/wrist_image` as observation keys. The `features_map` in `RoboMMEEnv` maps these to `observation.images.image` and `observation.images.wrist_image` for the policy. State is exposed as `agent_pos` and maps to `observation.state`.
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The dataset's `image` and `wrist_image` columns already align with the policy input keys, so no renaming is needed when fine-tuning.
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## Action Spaces
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| Type | Dim | Description |
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| ------------- | --- | --------------------------------------------------------- |
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| `joint_angle` | 8 | 7 joint angles + 1 gripper (−1 closed, +1 open, absolute) |
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| `ee_pose` | 7 | xyz + roll/pitch/yaw + gripper |
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Set via `--env.action_space=joint_angle` (default) or `--env.action_space=ee_pose`.
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## Platform Notes
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- **Linux only**: ManiSkill requires SAPIEN/Vulkan. macOS and Windows are not supported.
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- **GPU recommended**: Rendering is CPU-capable but slow; CUDA + Vulkan gives full speed.
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- **gymnasium / numpy conflict**: See installation note above. Docker image handles this automatically.
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- **ManiSkill fork**: `robomme` depends on a specific ManiSkill fork (`YinpeiDai/ManiSkill`), pulled in automatically via the `robomme` package.
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