Home › Robotics › ai2thor

ai2thor - AI Robotics Tool

Overview

ai2thor is an open-source simulated environment for Visual AI that provides high-quality, interactive indoor scenes and detailed household objects. It supports multi-agent setups, a wide range of actions, and diverse sensory data to enable embodied AI research such as navigation, manipulation, and common-sense reasoning.

Key Features

  • High-quality, interactive indoor scenes
  • Detailed household object models
  • Multi-agent simulation support
  • Wide range of actions available
  • Diverse sensory data outputs for perception
  • Designed for embodied AI research workflows

Ideal Use Cases

  • Training and evaluating navigation agents
  • Research on object manipulation and interaction
  • Studying common-sense reasoning in embodied agents
  • Multi-agent coordination and collaboration experiments
  • Synthetic data generation for perception models

Getting Started

  • Open the project's GitHub repository to review documentation
  • Clone the repository to your local machine
  • Install required dependencies per the repo's instructions
  • Run included example scenes to validate the installation
  • Integrate your agent code with the simulator APIs
  • Refer to the repository license and contribution guidelines

Pricing

Open-source project; no pricing disclosed. Check the repository for license and deployment considerations.

Key Information

  • Category: Robotics
  • Type: AI Robotics Tool
Visit Official Website

Related Tools

LeRobot

A comprehensive suite of models, datasets, and tools for real-world robotics, providing end-to-end learning resources in PyTorch for robotics AI applications.
FAST: Efficient Action Tokenization for Vision-Language-Action Models

A Hugging Face repository that provides a universal action tokenizer (FAST+) designed to map sequences of robot actions into dense, discrete tokens for training autoregressive vision-language-action models. It supports both usage of a pre-trained tokenizer and easy custom training on users' own data.
Genesis

Genesis is a physics simulation platform and generative data engine for general-purpose robotics and embodied AI learning. It features a universal physics engine, ultra-fast robotics simulation, photorealistic rendering, and a modular generative framework that converts natural language prompts into various data modalities.
NVIDIA Isaac GR00T N1

An open foundation model for generalized humanoid robot reasoning and manipulation. It accepts multimodal inputs (language and images) and uses a vision-language foundation model combined with a diffusion transformer head to denoise continuous actions, enabling robot control and fine-tuning for specific tasks and embodiments.
NVIDIA Isaac GR00T N1

The world’s first open foundation model for generalized humanoid reasoning and skills, aimed at advancing robotics systems and autonomous vehicle technology.
Reachy Mini

Reachy Mini is an expressive, open‐source robot designed for human-robot interaction, creative coding, and AI experimentation. Programmable in Python (with upcoming support for additional languages), it integrates with state-of-the-art Hugging Face models for speech, vision, and personality. Available as a kit in a lite or wireless version, it is intended for AI developers, researchers, educators, and hobbyists to build, test, and share real-world AI applications.