# RoboTwin 2.0

RoboTwin 2.0 is a **large-scale dual-arm manipulation benchmark** built on the SAPIEN physics engine. It provides a standardized evaluation protocol for bimanual robotic policies across 60 tasks with strong domain randomization (clutter, lighting, background, tabletop height, and language instructions).

- Paper: [RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation](https://robotwin-platform.github.io)
- GitHub: [RoboTwin-Platform/RoboTwin](https://github.com/RoboTwin-Platform/RoboTwin)
- Leaderboard: [robotwin-platform.github.io/leaderboard](https://robotwin-platform.github.io/leaderboard)
- Dataset: [lerobot/robotwin_unified](https://huggingface.co/datasets/lerobot/robotwin_unified)

## Overview

| Property      | Value                                                      |
| ------------- | ---------------------------------------------------------- |
| Tasks         | 60 dual-arm manipulation tasks                             |
| Robot         | Aloha-AgileX bimanual (14 DOF, 7 per arm)                  |
| Action space  | 14-dim joint-space, continuous in `[-1, 1]`                |
| Cameras       | `head_camera`, `front_camera`, `left_wrist`, `right_wrist` |
| Simulator     | SAPIEN (not MuJoCo)                                        |
| Eval protocol | 100 episodes/task, 50 demo_clean demonstrations            |
| Eval settings | **Easy** (`demo_clean`) and **Hard** (`demo_randomized`)   |

## Available tasks

RoboTwin 2.0 ships with 60 dual-arm manipulation tasks. The full list appears on the [leaderboard](https://robotwin-platform.github.io/leaderboard). Example tasks:

| Task                   | CLI name                 | Category         |
| ---------------------- | ------------------------ | ---------------- |
| Beat block with hammer | `beat_block_hammer`      | Tool use         |
| Open / close laptop    | `open_laptop`            | Articulated obj  |
| Stack blocks (2 / 3)   | `stack_blocks_two/three` | Stacking         |
| Pour water             | `pour_water`             | Deformable/fluid |
| Fold cloth             | `fold_cloth`             | Deformable       |
| Handover block         | `handover_block`         | Bimanual coord.  |
| Place shoes            | `place_shoes_left/right` | Precision place  |
| Scan object            | `scan_object`            | Mobile manip.    |

Pass a comma-separated list to `--env.task` to run multiple tasks in a single eval sweep.

## Dataset

The RoboTwin 2.0 dataset is available in **LeRobot v3.0 format** on the Hugging Face Hub:

```
lerobot/robotwin_unified
```

It contains over 100,000 pre-collected trajectories across all 60 tasks (79.6 GB, Apache 2.0 license). No format conversion is needed — it is already in the correct LeRobot v3.0 schema with video observations and action labels.

You can load it directly with the HF Datasets library:

```python
from datasets import load_dataset

ds = load_dataset("lerobot/robotwin_unified", split="train")
```

## Installation

RoboTwin 2.0 requires **Linux** with an NVIDIA GPU (CUDA 12.1 recommended). Installation takes approximately 20 minutes.

### 1. Create a conda environment

```bash
conda create -n robotwin python=3.10 -y
conda activate robotwin
```

### 2. Install LeRobot

```bash
git clone https://github.com/huggingface/lerobot.git
cd lerobot
pip install -e "."
```

### 3. Install RoboTwin 2.0

```bash
git clone https://github.com/RoboTwin-Platform/RoboTwin.git
cd RoboTwin
bash script/_install.sh
bash script/_download_assets.sh
```

The install script handles all Python dependencies including SAPIEN, CuRobo, mplib, and pytorch3d.

<Tip warning={true}>
If the automated install fails, install manually:

```bash
pip install -r requirements.txt
pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"
cd envs && git clone https://github.com/NVlabs/curobo.git && cd curobo
pip install -e . --no-build-isolation
```

Then apply the required mplib fix: in `mplib/planner.py` line 807, remove `or collide` from the conditional.

</Tip>

### 4. Add RoboTwin to PYTHONPATH

The RoboTwin task modules must be importable by LeRobot. From within the `RoboTwin/` directory:

```bash
export PYTHONPATH="${PYTHONPATH}:$(pwd)"
```

Add this to your shell profile to make it permanent.

## Evaluation

### Standard evaluation (recommended)

Evaluate a policy on a single task with the official protocol (100 episodes):

```bash
lerobot-eval \
  --policy.path="your-hf-policy-id" \
  --env.type=robotwin \
  --env.task=beat_block_hammer \
  --eval.batch_size=1 \
  --eval.n_episodes=100
```

### Single-task quick check

```bash
lerobot-eval \
  --policy.path="your-hf-policy-id" \
  --env.type=robotwin \
  --env.task=beat_block_hammer \
  --eval.batch_size=1 \
  --eval.n_episodes=5
```

### Multi-task sweep

Evaluate on several tasks in one run:

```bash
lerobot-eval \
  --policy.path="your-hf-policy-id" \
  --env.type=robotwin \
  --env.task=beat_block_hammer,click_bell,handover_block,stack_blocks_two \
  --eval.batch_size=1 \
  --eval.n_episodes=100
```

### Full benchmark (all 60 tasks)

```bash
lerobot-eval \
  --policy.path="your-hf-policy-id" \
  --env.type=robotwin \
  --env.task=adjust_bottle,beat_block_hammer,blocks_ranking_rgb,blocks_ranking_size,click_alarmclock,click_bell,close_laptop,close_microwave,dump_bin,grab_roller,handover_block,handover_cup,handover_diverse_bottles,handover_mic,hanging_mug,insert_pin,lift_pot,make_tea,open_laptop,open_microwave,pick_diverse_bottles,pick_dual_bottles,place_basket,place_block,place_cable,place_can,place_chopsticks,place_cloth,place_container,place_cup,place_diverse_bottles,place_dual_bottles,place_fork,place_knife,place_object_basket,place_ring,place_ruler,place_shoes_left,place_shoes_right,place_spoon,place_toy,pour_water,press_stapler,put_bottles_dustbin,put_object_cabinet,put_shoes_box,rotate_qrcode,scan_object,shake_bottle,shake_bottle_horizontally,stack_blocks_three,stack_blocks_two,stack_bowls_three,stack_bowls_two,stamp_seal,turn_switch,wipe_board,arrange_tools,build_tower,fold_cloth \
  --eval.batch_size=1 \
  --eval.n_episodes=100
```

## Camera configuration

By default, all four cameras are included:

| Camera key     | Description                    |
| -------------- | ------------------------------ |
| `head_camera`  | Overhead / third-person view   |
| `front_camera` | Front-facing static camera     |
| `left_wrist`   | Left arm wrist-mounted camera  |
| `right_wrist`  | Right arm wrist-mounted camera |

To use a subset of cameras, override `--env.camera_names`:

```bash
lerobot-eval \
  --policy.path="your-hf-policy-id" \
  --env.type=robotwin \
  --env.task=beat_block_hammer \
  --env.camera_names="head_camera,left_wrist,right_wrist" \
  --eval.batch_size=1 \
  --eval.n_episodes=10
```

## Environment config reference

Key parameters for `RoboTwinEnvConfig`:

| Parameter            | Default                                             | Description                        |
| -------------------- | --------------------------------------------------- | ---------------------------------- |
| `task`               | `"beat_block_hammer"`                               | Comma-separated task name(s)       |
| `fps`                | `25`                                                | Simulation FPS                     |
| `episode_length`     | `300`                                               | Max steps per episode              |
| `obs_type`           | `"pixels_agent_pos"`                                | `"pixels"` or `"pixels_agent_pos"` |
| `camera_names`       | `"head_camera,front_camera,left_wrist,right_wrist"` | Comma-separated active cameras     |
| `observation_height` | `480`                                               | Camera pixel height                |
| `observation_width`  | `640`                                               | Camera pixel width                 |

## Leaderboard submission

Results can be submitted to the [RoboTwin 2.0 leaderboard](https://robotwin-platform.github.io/leaderboard). The official protocol requires:

- Training on 50 `demo_clean` demonstrations per task
- Evaluating 100 episodes per task
- Reporting success rate separately for **Easy** (`demo_clean`) and **Hard** (`demo_randomized`) settings

For submission instructions, refer to the [RoboTwin 2.0 documentation](https://robotwin-platform.github.io/doc/).