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Update pre-commit-config.yaml + pyproject.toml + ceil rerun & transformer dependencies version (#1520)

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* chore: update .gitignore

* chore: update pre-commit

* chore(deps): update pyproject

* fix(ci): multiple fixes

* chore: pre-commit apply

* chore: address review comments

* Update pyproject.toml

Co-authored-by: Ben Zhang <5977478+ben-z@users.noreply.github.com>
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>

* chore(deps): add todo

---------

Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Ben Zhang <5977478+ben-z@users.noreply.github.com>
This commit is contained in:
Steven Palma
2025-07-17 14:30:20 +02:00
committed by GitHub
parent 0938a1d816
commit 378e1f0338
78 changed files with 1450 additions and 636 deletions
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24
docs/source/hilserl_sim.mdx
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@@ -11,7 +11,6 @@ This guide explains how to use the `gym_hil` simulation environments as an alter
Currently, the main environment is a Franka Panda robot simulation based on MuJoCo, with tasks like picking up a cube.
## Installation
First, install the `gym_hil` package within the LeRobot environment:
@@ -25,8 +24,6 @@ pip install -e ".[hilserl]"
- A gamepad or keyboard to control the robot
- A Nvidia GPU
## Configuration
To use `gym_hil` with LeRobot, you need to create a configuration file. An example is provided [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/gym_hil_env.json). Key configuration sections include:
@@ -35,14 +32,15 @@ To use `gym_hil` with LeRobot, you need to create a configuration file. An examp
```json
{
"type": "hil",
"name": "franka_sim",
"task": "PandaPickCubeGamepad-v0",
"device": "cuda"
"type": "hil",
"name": "franka_sim",
"task": "PandaPickCubeGamepad-v0",
"device": "cuda"
}
```
Available tasks:
- `PandaPickCubeBase-v0`: Basic environment
- `PandaPickCubeGamepad-v0`: With gamepad control
- `PandaPickCubeKeyboard-v0`: With keyboard control
@@ -65,6 +63,7 @@ Available tasks:
```
Important parameters:
- `gripper_penalty`: Penalty for excessive gripper movement
- `use_gripper`: Whether to enable gripper control
- `end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector
@@ -76,40 +75,49 @@ Important parameters:
To run the environment, set mode to null:
<!-- prettier-ignore-start -->
```python
python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
```
<!-- prettier-ignore-end -->
### Recording a Dataset
To collect a dataset, set the mode to `record` whilst defining the repo_id and number of episodes to record:
<!-- prettier-ignore-start -->
```python
python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
```
<!-- prettier-ignore-end -->
### Training a Policy
To train a policy, checkout the configuration example available [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/train_gym_hil_env.json) and run the actor and learner servers:
<!-- prettier-ignore-start -->
```python
python -m lerobot.scripts.rl.actor --config_path path/to/train_gym_hil_env.json
```
<!-- prettier-ignore-end -->
In a different terminal, run the learner server:
<!-- prettier-ignore-start -->
```python
python -m lerobot.scripts.rl.learner --config_path path/to/train_gym_hil_env.json
```
<!-- prettier-ignore-end -->
The simulation environment provides a safe and repeatable way to develop and test your Human-In-the-Loop reinforcement learning components before deploying to real robots.
Congrats 🎉, you have finished this tutorial!
> [!TIP]
> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
Paper citation:
```
@article{luo2024precise,
title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},