---
title: Environment Reference
description: Complete reference for all registered simulation environments.
---

# Environment Reference

SO101-Nexus provides Gymnasium-compatible MuJoCo environments for the SO-101 arm. Each environment is registered with a unique ID and can be created with `gym.make()`.

## All Environments

| ID | Backend | Task | Max Steps |
|----|---------|------|-----------|
| [`MuJoCoPickLift-v1`](/docs/environments/mujoco-pick-lift) | MuJoCo | Pick and lift an object | 1024 |
| [`MuJoCoPickAndPlace-v1`](/docs/environments/mujoco-pick-and-place) | MuJoCo | Pick a cube and place on target | 1024 |
| [`MuJoCoTouch-v1`](/docs/environments/mujoco-touch) | MuJoCo | Touch an object on the table | 512 |
| [`MuJoCoLookAt-v1`](/docs/environments/mujoco-look-at) | MuJoCo | Orient TCP toward object | 256 |
| [`MuJoCoMove-v1`](/docs/environments/mujoco-move) | MuJoCo | Move TCP in a direction | 256 |

## Registration

MuJoCo environments are registered when you import `so101_nexus.mujoco`.

```python
import gymnasium as gym
import so101_nexus.mujoco

env = gym.make("MuJoCoPickLift-v1")
```

## Train a policy in Colab

Every task has a GPU-parallel Warp counterpart (`Warp*-v1`); PickLift, Touch, LookAt, and Move have solved PPO baselines you can reproduce in Colab (PickAndPlace's Warp baseline is pending a task fix, see [Training with PPO](/docs/training/ppo)).

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/johnsutor/so101-nexus/blob/main/examples/ppo_warp_colab.ipynb)

Pick the task with `ENV_ID` in the notebook's environment cell. See [Training with PPO](/docs/training/ppo) for details and per-task results.
