---
title: config_examples
canonical_url: "https://www.modelscope.cn/datasets/lerobot/config_examples"
md_url: "https://www.modelscope.cn/datasets/lerobot/config_examples.md"
repository: lerobot/config_examples
last_updated: 2026-05-11
license: "Apache License 2.0"
storage_size: "31 KB"
downloads: 150
stars: 0
---

# config_examples

> config_examples - lerobot 在 ModelScope 开源的数据集。A repository that contains example json files that can be used for different applications of the LeRobot code base.

lerobot/config_examples 是 ModelScope 魔搭社区上的数据集，存储大小 31 KB，采用 Apache License 2.0 许可。

- **Repository**: lerobot/config_examples
- **License**: Apache License 2.0
- **Storage size**: 31 KB
- **Downloads**: 150
- **Stars**: 0
- **Last updated**: 2026-05-11

Source: https://www.modelscope.cn/datasets/lerobot/config_examples

---

A repository that contains example json files that can be used for different applications of the LeRobot code base.

Current available configs:

  - RL :
    - env_config.json: Environment config for a real robot setup using a gamepad for teleoperation and an SO10* arm as the main agent. Using `gym_manipulator.py`, one can use this config to teleoperate the robot and record a dataset for reinforcement learning.
    - train_config.json: Training config for the HIL-SERL implementation in LeRobot on the real robot using the similar environment configuration to the `env_config.json` in the same directory. 
    - gym_hil:
      - env_config.json: Environment config for simulation using the `gym_hil` environment. 
      - train_config.json: Training config for the HIL-SERL implementation in LeRobot for simulated environments.
  - Sim IL:
    - env_config.json: Environment config for simulation using the `gym_hil` environment. You can use this configuration to collect a dataset in simulation that can be used for imitation learning or reinforcement learning. 
    - eval_config.json: Evaluation config for models trained on datasets collected from `gym_hil` environment.
  - Reward Classifier:
    - config.json: Main configuration for training a reward classifier.