---
license: mit
library_name: lerobot
tags: [lerobot, envhub, robotics, simulation, benchmark, ladder, widowx, maniskill, sapien]
---

# LADDER SimplerEnv-Long

54 WidowX tasks of 1 to 10 pick-and-place subgoals in the Bridge digital twin of SimplerEnv, on SAPIEN 3 / ManiSkill 3. It is one of the three suites of [LADDER](https://ladderrobotics.ai), a long-horizon manipulation benchmark, packaged as a LeRobot [EnvHub](https://huggingface.co/docs/lerobot/envhub) environment. Each simulator needs its own Python environment, so each suite is its own repository:

| Suite | Repository | Simulator | Robot | Tasks |
|---|---|---|---|---|
| RoboLab-Long | [`INSAIT-Institute/LADDER-RoboLab-Long`](https://huggingface.co/INSAIT-Institute/LADDER-RoboLab-Long) | Isaac Sim 6 | Franka (DROID) | 44 |
| SimplerEnv-Long | this one | SAPIEN 3 / ManiSkill 3 | WidowX | 54 |
| RobotArena-∞-Long | [`INSAIT-Institute/LADDER-RobotArena-Long`](https://huggingface.co/INSAIT-Institute/LADDER-RobotArena-Long) | Genesis | WidowX | 47 |

## Evaluate a policy

From a LeRobot checkout, with Docker and the NVIDIA Container Toolkit:

```bash
pip install -e ".[dataset,evaluation,remote]"  # plus your policy's extra
lerobot-env-server --envhub=INSAIT-Institute/LADDER-SimplerEnv-Long@v1.0.0 --docker=true
lerobot-eval --policy.path=<your WidowX checkpoint> \
  --env.type=envhub --env.hub_path=INSAIT-Institute/LADDER-SimplerEnv-Long@v1.0.0 \
  --env.server.endpoint=tcp/127.0.0.1:7448 --env.task=all2_plate \
  --eval.batch_size=24 --eval.n_episodes=24 --eval.use_async_envs=false
```

- The first `--docker=true` builds the simulator image (about 11 GB, 10 to 20 minutes); later starts reuse it.
- `--env.task` takes a task, a comma-separated list, or nothing for the whole suite ([task list](https://github.com/insait-institute/ladder/blob/main/TASKS.md)). A task's 24 episodes run as one batch, so keep the batch size at 24.
- To run it in-process instead, `pip install "lerobot[dataset,evaluation]>=0.7.0" -r requirements.txt` and replace `--env.server.endpoint` with `--env.trust_remote_code=true`.

## Interface

| | |
|---|---|
| Observation | `pixels.main`: 480x640 RGB; `agent_pos`: end-effector pose `[x, y, z, rx, ry, rz]` (base frame, axis-angle, BridgeData convention) and gripper (1 open) |
| Action | `[dx, dy, dz, drx, dry, drz, gripper]`: translation in the base frame, rotation in the end-effector frame, applied to the commanded pose as LADDER runs its Bridge policy; gripper 1 open, 0 closed |
| Control rate | 5 Hz |
| Score | `pc_success`: LADDER's task success. `avg_sum_reward`: its subgoal progress at the end of the episode (also `info["metrics"]["subgoal_progress"]`) |

## Relation to LADDER

`ladder/` is LADDER's code at release [v1.0.0](https://github.com/insait-institute/ladder/releases/tag/v1.0.0), unchanged, run with the settings of LADDER's own server. The simulation assets (160 MB) are downloaded from that release on first use. Its reset frames match LADDER's recorded episodes, and LADDER's Bridge policy run through it succeeds as often as in LADDER's published episodes (60 and 64 of 96 against 59 on four tasks). LADDER's own harness stays the reference for the [leaderboard](https://ladderrobotics.ai).

## Citation

```bibtex
@article{ladder2026,
  title   = {{LADDER}: A Long-Horizon Robot Manipulation Benchmark},
  author  = {Petkov, Petko and Albanese, Giuliano and Nikolov, Nikolay and Van Gool, Luc and Tremblay, Jonathan and Paudel, Danda Pani},
  journal = {arXiv preprint arXiv:2610.18053},
  year    = {2026},
  url     = {https://arxiv.org/abs/2610.18053}
}
```

## License

MIT, as LADDER. Downloaded assets keep their own licenses ([THIRD_PARTY_NOTICES.md](https://github.com/insait-institute/ladder/blob/main/THIRD_PARTY_NOTICES.md)).