unienv 0.0.1b13

Unified robot environment framework supporting multiple tensor and simulation backends

UniEnv

UniEnv is a Python framework for building robot environments and robot data pipelines on top of a shared set of abstractions.

Documentation: https://unienvorg.github.io/UniEnv/

It gives you:

• a common environment interface for simulation and real-robot control
• a functional environment variant for explicit state passing
• backend-aware spaces, wrappers, and transformations
• world and node composition utilities for multi-component environments
• replay buffers, storages, samplers, and dataset adapters for offline data workflows

The project is designed around one idea: environment code and dataset code should not have to be rewritten every time the simulator, robot, or tensor library changes.

What Is In The Package

• unienv_interface: environments, worlds, nodes, spaces, wrappers, and transformations
• unienv_data: batches, replay buffers, storage backends, samplers, and dataset integrations
• backend portability through XBArray and DLPack-based conversion paths

Installation

Install the base package:

pip install unienv

Install optional extras when needed:

pip install "unienv[gymnasium,video]"

Some integrations and storage backends rely on their own ecosystem packages such as pyarrow, h5py, datasets, huggingface_hub, torch, or jax. Install the ones that match the features you plan to use.

Quick Example

The example below shows the core building blocks without depending on a specific simulator:

import numpy as np

from unienv_interface.backends.numpy import NumpyComputeBackend
from unienv_interface.space.spaces import BoxSpace, DictSpace
from unienv_interface.transformations import RescaleTransformation
from unienv_data.replay_buffer import ReplayBuffer
from unienv_data.storages.parquet import ParquetStorage

backend = NumpyComputeBackend

transition_space = DictSpace(
    backend,
    {
        "obs": BoxSpace(backend, 0.0, 1.0, np.float32, shape=(4,)),
        "action": BoxSpace(backend, -1.0, 1.0, np.float32, shape=(2,)),
        "reward": BoxSpace(backend, -np.inf, np.inf, np.float32, shape=()),
    },
)

action_transform = RescaleTransformation(new_low=0.0, new_high=1.0)
normalized_action_space = action_transform.get_target_space_from_source(
    transition_space["action"]
)

buffer = ReplayBuffer.create(
    ParquetStorage,
    transition_space,
    cache_path="cache/demo_buffer",
    capacity=10_000,
)

print(normalized_action_space)
print(buffer.single_space)

In practice, you would pair these components with your own Env, FuncEnv, World, or WorldNode implementations, then add wrappers and storage backends as needed.

Documentation

Documentation: https://unienvorg.github.io/UniEnv/

Start with:

• docs/getting-started.md for installation and the package map
• docs/concepts/ for the core abstractions
• docs/guides/ for wrappers, replay buffers, and dataset integrations

Development

For local development:

git clone https://github.com/UniEnvOrg/UniEnv
cd UniEnv/UniEnvPy
pip install -e .[dev,gymnasium,video]
pytest

If you want to exercise optional backends or integrations during development, install their matching dependencies explicitly before running the relevant tests.

Cite

If you use UniEnv in research, cite:

@software{cao_unienv,
  author = {Yunhao Cao AND Cory Fan AND Meryl Zhang AND Sabrina Liu AND Kuan Fang},
  title = {{UniEnv: Unifying Robot Environments and Data APIs}},
  year = {2025},
  month = oct,
  url = {https://github.com/UniEnvOrg/UniEnv},
  license = {MIT}
}

Acknowledgements

UniEnv is influenced by Gymnasium and OpenAI Gym, and builds on ideas from the Array API Standard and DLPack.