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Basic Information

Habitat-Lab is a modular, high-level library for end-to-end development and research in embodied AI. It provides infrastructure to define and run embodied tasks in realistic indoor simulation using Habitat-Sim as the core simulator. The repository is intended for training and evaluating embodied agents on tasks such as navigation, rearrangement, instruction following, question answering, and human following. It supports single- and multi-agent setups, configurable agent embodiments and sensors, human-in-the-loop interaction for data collection and evaluation, and integration with robotics tools. The README documents installation steps using conda and pip, examples for non-interactive and interactive testing, dataset download utilities, available baselines, debugging tips for vectorized environments, and Docker containers for reproducible setups. The project is MIT licensed and intended for researchers and developers building embodied AI systems.

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App Details

Features
A flexible task and configuration system that lets users specify a wide variety of single- and multi-agent embodied tasks and override configuration keys. Support for diverse embodied agents and sensors, including virtual robots and humanoids, with configurable capabilities. Training and evaluation tooling with provided baselines including reinforcement learning via PPO, imitation and non-learning pipelines such as SensePlanAct. Human-in-the-loop interaction support for collecting embodied datasets and interacting with agents. Integration with Habitat-Sim as the core physics and rendering simulator and optional Bullet physics. Example scripts for pick tasks and interactive play, dataset download utilities, debugging advice for vectorized environments, Docker images for GPU-enabled reproducible environments, and guidance for extending sensors and measures.
Use Cases
Habitat-Lab gives researchers and developers a common platform to build, train, debug and benchmark embodied agents in realistic indoor simulations. It lowers engineering overhead by providing reusable task definitions, modular agent and sensor configuration, dataset utilities, and example scripts that illustrate training and interactive control. Built-in baselines and evaluation metrics enable reproducible comparisons and faster iteration on algorithms such as PPO or imitation learning. Human-in-the-loop capabilities permit data collection and human-agent experiments. Docker containers and documented conda and pip installs simplify reproducible environments. Debugging tips for vectorized environments and examples to extend sensors and measures help adapt the stack to new research questions or robotic platforms. ROS bridging information supports integration with external robotics ecosystems.

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