loopquest 0.1.4

A Production Tool for Embodied AI.

:scroll:Loopquest

A Production Tool for Embodied AI.

• :video_camera:Tutorial Video
• :house:Discord

Major features

• Log all the observation, action, reward, rendered images into database with only ONE extra line of code.

env = gymnasium.make("MountainCarContinuous-v0", render_mode="rgb_array")

->

env = LoopquestGymWrapper(
    gymnasium.make("MountainCarContinuous-v0", render_mode="rgb_array")
)

• Beautiful frontend to visualize all the data and rendered images / videos of the simulation environment.
• Compare your experiments in an intuitive way.
• Directly trainable data for robotics foundation model. Select and download the (observation, action, reward) data with the dataloader interfaces of the most popular deep learning frameworks (e.g. tensorflow, pytorch, huggingface dataset apis).

Installation

For stable version, run

pip install loopquest

For dev version or loopquest project contributors, clone the git to your local machine by running

git clone https://github.com/LoopMind-AI/loopquest.git

Change to the project root folder and install the package

cd loopquest
pip install -e .

How to run (dev only for now)

At loopquest folder, bring up the backend server and database,

docker compose up --build

Bring up the frontend web app,

cd frontend
npm run dev

Then run quickstart script,

python examples/quickstart.py

The command prompt should mention "Check your experiment progress on http://localhost:3000/experiment/<exp_id>".

Quick Start Example

import gymnasium
from loopquest.gym_wrappers import LoopquestGymWrapper

experiment_name = "test"
env = LoopquestGymWrapper(
    gymnasium.make("MountainCarContinuous-v0", render_mode="rgb_array"),
    experiment_name,
)
obs, info = env.reset()
for i in range(100):
    action = env.action_space.sample()
    obs, reward, terminated, truncated, info = env.step(action)
    rgb_array = env.render()
    if terminated or truncated:
        break
env.close()