VirtualHome is an interactive platform designed to simulate complex household activities using programs and a Python API. It supports rich environmental interactions, multi-agent activities, and serves as an environment for embodied AI and reinforcement learning agent training.

Is virtualhome open source?

Please check the official repository for license details.

virtualhome: An interactive platform designed to simulate complex household… (★ 623)

Features

01Simulates complex household activities through programs via a Python API.

02Enables rich interactions with the environment, including picking up objects and operating appliances.

03Features procedural generation to create an infinite variety of unique environments.

04Supports multi-agent activities and allows dynamic modification of environments.

05Provides streaming of ground-truth data (segmentation, optical flow, depth) and OpenAI Gym-like RL environments.

Why choose it

+Simulates complex household activities through programs via a Python API.

+Training agents for embodied AI and Reinforcement Learning tasks.

+Covers 6 supported environments or platforms, which is helpful for broader deployment needs.

+The latest recorded update is 2026-05-20, which suggests the project is still actively maintained.

Trade-offs

!There are at least 8 related tools in the same category, so the best choice is easier to make after side-by-side comparison.

Compatibility

Python

Native

Verified via docs

Unity

Integrated

Verified via docs

Google Colab

Supported

Verified via docs

Jupyter

Supported

Verified via docs

Ray

Supported

Verified via docs

Docker

Supported

Verified via docs

Quick start

$ pip install virtualhome

Use cases

↳Training agents for embodied AI and Reinforcement Learning tasks.

↳Simulating and visualizing complex household activities through programs.

↳Generating large-scale datasets of programs and environment states for research.

How it compares

≈virtualhome sits in the Vision / Multimodal category, so it makes more sense to evaluate it alongside tools like ragflow instead of in isolation.

≈If your main need is closer to "Training agents for embodied AI and Reinforcement Learning tasks.", that use case is a better lens for comparison than broad feature checklists alone.

≈virtualhome's licensing and community traction are both easier to judge in category context.