What is VirtualHome?
VirtualHome is an innovative platform designed to simulate complex household activities through a series of programmable tasks. Its standout feature is the ability to interact with different objects within a virtual environment, such as picking up objects or turning appliances on and off. Users can create detailed sequences of actions using a simple Python API, which are then visually represented within the VirtualHome platform. The flexibility of choosing various agents and environments adds to the customizable nature of the simulation. Furthermore, it supports the streaming of real-time data such as actions, segmentations, and depth information, making it a valuable tool for developing AI in embodied environments.
Latest Updates in VirtualHome 2.3
VirtualHome version 2.3 comes packed with exciting new features:
- Procedural Generation: Offers an endless array of unique environments for agents to explore.
- Enhanced Physics: Realistic interactions with the environment’s physics have been improved.
- Time System: A synchronized day-night cycle adds a layer of realism.
- Realistic Lighting: Outdoor environments feature accurate sunlight and shadows; indoor scenes have been upgraded with better real-time lighting.
- Room Design: The addition of more lifelike rooms enhances immersion.
- Performance Boosts: Significant improvements include faster performance and greater stability.
- Streamlined Documentation: A new guide helps users navigate the platform more efficiently.
- Bug Fixes: Resolves previously existing issues for smoother operation.
How Does VirtualHome Work?
Activities are simulated in VirtualHome through two main elements:
- Programs: These outline the sequence of actions for an activity.
- Graphs: These represent the environment where the activity happens.
Two types of simulators are employed:
- Unity Simulator: Built using Unity, this simulator generates visual content showcasing activities.
- Evolving Graph Simulator: Operates entirely in Python, creating a sequence of environment-based graphs as the program executes.
Getting Started with VirtualHome
Installation
Install the VirtualHome package using Python's package manager:
$ pip install virtualhome
A Jupyter notebook demonstration is also available to help users get started.
Downloading and Testing Unity Simulator
To use the Unity Simulator, download the appropriate executable for your operating system and follow the provided instructions to set it up locally or remotely.
Running Simulations
Once the simulator is installed, videos and snapshots of activities can be generated using provided scripts. These help visualize how the virtual environment changes over time during different scenarios.
Using VirtualHome for Reinforcement Learning
VirtualHome can serve as an extensive environment for training AI agents through Reinforcement Learning. The base class UnityEnvironment helps manage and explore the environment’s capabilities, with potential for parallel operations using frameworks like Ray.
Datasets and Resources
VirtualHome offers access to datasets that include both real-world programs and those generated through scripts. Users can also access additional resources to enhance the performance and capabilities of the simulator.
Contributing to VirtualHome
The platform is open to contributions from developers. Interested users can access the Unity source code, make enhancements, and build custom versions of the simulator.
Recognition and Academic Use
VirtualHome has been recognized and cited in various academic publications. Anyone intending to use the simulator for research or professional work is encouraged to reference the associated publications.
Contributors
VirtualHome has been developed and improved by a team of dedicated experts, including Xavier Puig, Marko Boben, Kabir Swain, and several others who have made significant contributions to its development.
VirtualHome represents a cutting-edge platform pushing the boundaries of AI training and simulation within virtual household environments. Whether you’re a researcher, developer, or AI enthusiast, it provides a robust toolset for exploring complex simulations and interactions.