facebookresearch/habitat-lab
A modular high-level library to train embodied AI agents across a variety of tasks and environments.
What it solves
Habitat-Lab 提供了一个用于具身智能端到端开发的模块化框架。它解决了在室内环境中训练智能体执行复杂任务（例如导航、物体重新排列和遵循人类指令）的挑战，同时提供评估其性能的工具，并允许人类与模拟环境进行互动。
How it works
该库建立在 Habitat-Sim 核心模拟器之上，允许开发者：
- Define Tasks: Create flexible single or multi-agent tasks including question answering, human following, and navigation.
- - Configure Agents: Instantiate various embodied agents, ranging from humanoids to commercial robots, by specifying their sensors and capabilities.
- Train and Evaluate: Use provided algorithms for reinforcement learning (n/a) (including PPO baselines), imitation learning, or non-learning pipelines (SensePlanAct) to train agents and benchmark them using standard metrics.
- Human Interaction: Use a framework that lets humans interact with the simulator to train agents and benchmark them using standard metrics.
Who it’s for
此项目是为从事具身智能、机器人模拟以及自主智能体与室内环境交互的研究人员和开发者设计的。
Highlights
Modular Design: Supports a wide variety of task definitions and agent configurations.
Diverse Agent Support: Compatible with various robot types and humanoid models.
- Integrated Baselines: Includes reinforcement learning baselines via PPO.
Human-in-the-Loop: Enables direct human interaction with the simulated environment for data collection.
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