VirtualHome: Simulating Household Activities via Programs (1806.07011v1)

Abstract: In this paper, we are interested in modeling complex activities that occur in a typical household. We propose to use programs, i.e., sequences of atomic actions and interactions, as a high level representation of complex tasks. Programs are interesting because they provide a non-ambiguous representation of a task, and allow agents to execute them. However, nowadays, there is no database providing this type of information. Towards this goal, we first crowd-source programs for a variety of activities that happen in people's homes, via a game-like interface used for teaching kids how to code. Using the collected dataset, we show how we can learn to extract programs directly from natural language descriptions or from videos. We then implement the most common atomic (inter)actions in the Unity3D game engine, and use our programs to "drive" an artificial agent to execute tasks in a simulated household environment. Our VirtualHome simulator allows us to create a large activity video dataset with rich ground-truth, enabling training and testing of video understanding models. We further showcase examples of our agent performing tasks in our VirtualHome based on language descriptions.

• The paper introduces a scalable framework for simulating household tasks using executable symbolic programs derived from crowdsourced data.
• The methodology leverages a Unity3D-based simulator that integrates atomic actions for navigation and object manipulation in realistic settings.
• The results facilitate training video understanding models and advance natural language-driven autonomous robotics for household applications.

VirtualHome: Simulating Household Activities via Programs

The paper "VirtualHome: Simulating Household Activities via Programs" presents a computational framework for modeling complex activities in typical household environments using executable programs. The primary goal is to provide a non-ambiguous representation for tasks that can be utilized by autonomous agents to execute various household activities. Traditional direct programming methods are limited by scalability due to the diversity and number of daily tasks, while this approach offers a scalable solution through the use of symbolic programs.

Key Contributions

Several key contributions are outlined in this research:

1. Crowdsourced Data Collection: The authors initiate by crowdsourcing a comprehensive dataset of household activities encoded as programs. These programs are created through a game-based interface leveraging the Scratch platform, where crowd workers translate natural language descriptions into sequences of atomic actions.
2. VirtualHome Simulator: The researchers have developed VirtualHome, a simulator built on the Unity3D game engine. This simulator brings together atomic actions such as navigation, object manipulation, and environmental interaction to execute the scripted programs. As a result, VirtualHome provides an interactive medium to synthesize rich datasets of activity videos with precise ground-truth needed for training and validating video understanding models.
3. Automatic Program Generation: A critical technological advancement made in the paper is the ability to infer executable programs from expressive natural language descriptions and from video inputs. Through this, naive users can potentially teach robots new tasks, enabling broader application of household robotic systems.
4. Language and Vision System Training: VirtualHome generates a significant dataset that facilitates the training and testing of systems focused on video understanding. By studying how agents perform tasks from video demonstrations and inferring tasks from such data, the framework provides a foundation for integrating symbolic reasoning in robotics using visual and textual input.

Insights and Implications

This research holds practical implications for developing household robotics.—specifically, in moving from programming explicit actions to high-level task definitions that integrate seamlessly with natural language interfaces. The framework ensures the interpretation of complex sequences of actions by mapping them onto decomposable and executable scripts, thereby enhancing an agent's ability to understand and perform tasks autonomously in household settings.

From a theoretical standpoint, the paper underscores the potential of combining symbolic programming with machine learning techniques for behavior modeling in environments with inherent complexities and immense variability.

Future developments could focus on enhancing the richness of the ground-truth data generated by the simulator, expanding the repertoire of atomic actions modeled within VirtualHome, and improving the generalization capabilities of the learning algorithms to unseen environments or activities. Additionally, the integration of reinforcement learning within this framework could further extend the capabilities of autonomous agents, allowing for the adaptive learning of tasks based on environmental feedback.

In conclusion, "VirtualHome: Simulating Household Activities via Programs" contributes a sophisticated blend of symbolic AI and simulation-based learning, setting the stage for further exploration into autonomous agents and robotic applications in home environments. The framework represents a significant step toward enabling intelligent systems to interact with complex everyday environments, thereby promoting more seamless and intuitive human-robot interactions.