gym-aloha
A gym environment for ALOHA
Decision gist · record as of 2026-08-14
Yes, if you are working on reinforcement learning or robot control research and need a standardized ALOHA simulation environment. The package has low install friction, permissive licensing, no known vulnerabilities, and active maintenance. Caveat: requires Python 3.10+, and GPU rendering requires careful MuJoCo/EGL setup; CPU rendering will work but may be slow for large-scale training.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10+.
- MuJoCo rendering may silently fall back to CPU unless EGL is properly configured on GPU systems.
- Low friction—pure Python wheel with four runtime dependencies (dm-control, gymnasium, imageio, mujoco).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in commercial and private projects with minimal restrictions; attribution required.
last release 2026-06-10 (65 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 106,817 downloads/mo, #12,637 on PyPI
Alternatives
Verify before relying
pip install gym-aloha
import gymnasium as gym
import gym_aloha
env = gym.make("gym_aloha/AlohaInsertion-v0")
observation, info = env.reset()
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)
env.close()- Whether the package has active community support or issue response time beyond the maintenance status flag.
- Performance characteristics (steps/sec, memory usage) for typical training workloads.
- Compatibility with specific versions of dm-control, gymnasium, or mujoco beyond the declared runtime dependencies.
What it is and what it does
gym-aloha wraps the ALOHA dual-arm robotic manipulation system as a Gymnasium environment, allowing reinforcement learning researchers and practitioners to train agents on two concrete manipulation tasks: TransferCubeTask (pick and transfer a cube between grippers) and InsertionTask (grasp and insert a peg into a socket). The environment is built on MuJoCo physics simulation and dm-control, providing continuous 14-dimensional action spaces (joint positions and gripper states for two arms) and multi-modal observations (joint states, camera images from multiple angles, and environment state). Rewards are structured as sparse milestones (1–4 points per task) based on task progress.
The package is designed for researchers developing imitation learning, reinforcement learning, or other control algorithms on realistic manipulation tasks. It supports configurable observation types (pixels or pixels with agent position), image resolution, and rendering modes. GPU-accelerated rendering is available but requires careful EGL configuration. The environment is in alpha status and actively maintained, with support for Python 3.10 through 3.14.
Use it for
- Train reinforcement learning agents on dual-arm manipulation tasks with sparse reward signals and visual observations.
- Develop imitation learning pipelines using the ALOHA environment as a standardized benchmark.
- Prototype robot control policies in simulation before deployment to physical ALOHA hardware.
- Research multi-modal learning approaches combining joint state, velocity, and camera image observations.
- Benchmark manipulation algorithms on structured insertion and transfer tasks with configurable difficulty.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working on reinforcement learning or robot control research and need a standardized ALOHA simulation environment.
The package has low install friction, permissive licensing, no known vulnerabilities, and active maintenance. Caveat: requires Python 3.10+, and GPU rendering requires careful MuJoCo/EGL setup; CPU rendering will work but may be slow for large-scale training.
Install
gym-aloha on PyPI
Before you install
Low friction—pure Python wheel with four runtime dependencies (dm-control, gymnasium, imageio, mujoco). Actively maintained as of 65 days since release. Python 3.10+ required; MuJoCo itself may require GPU/EGL setup for optimal rendering performance.
Requires Python 3.10+. MuJoCo rendering may silently fall back to CPU unless EGL is properly configured on GPU systems.
License in practice
Apache-2.0 permissive license allows use in commercial and private projects with minimal restrictions; attribution required.
Quickstart
pip install gym-aloha
import gymnasium as gym
import gym_aloha
env = gym.make("gym_aloha/AlohaInsertion-v0")
observation, info = env.reset()
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)
env.close()Verify before relying
- Whether the package has active community support or issue response time beyond the maintenance status flag.
- Performance characteristics (steps/sec, memory usage) for typical training workloads.
- Compatibility with specific versions of dm-control, gymnasium, or mujoco beyond the declared runtime dependencies.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesdm-controlgymnasiumimageiomujoco |
| Maintenance | Actively maintained 65 days since the last release |
| First released | |
| Downloads | 106,817 / month, #12,637 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Software Development :: Build Tools |
Evidence: gym_aloha-0.1.4-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “aloha robot simulation environment”
- gym-alohaProvides a Gymnasium environment for the ALOHA dual-arm robotic…
- sapienSAPIEN is a physics-rich simulation environment for articulated…
- robosuiterobosuite is a MuJoCo-powered simulation framework for building,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Build Tools packages
Provides reusable utilities for Python packaging interoperability, including version handling, specifiers, markers, requirements, tags, and metadata parsing according to standards like PEP 440 and PEP 425.
Wraps any iterable to display a real-time progress bar in the terminal or Jupyter notebook, showing iteration count, elapsed time, and estimated time remaining.
pip is the standard installer for Python packages, enabling you to download and install packages from the Python Package Index and other indexes into your Python environment.
Hatchling is a standards-compliant Python build backend that handles packaging, metadata, and distribution of Python projects when configured in a project's pyproject.toml file.
Generates Python gRPC service stubs and message classes from Protocol Buffer definitions, enabling developers to build gRPC clients and servers.
pre-commit is a framework for installing and running git hooks written in any language before commits are made, automating code quality and validation checks across multi-language projects.
Install it if your team needs consistent, automated validation at commit time.
See also gymnasium · dm-control · Shimmy · skrl · fhaviary · mink · pettingzoo · stable-baselines3 · rsl-rl-lib · openenv-core