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 提供了一個用於具身智能端到端開發的模組化框架。它解決了在室內環境中訓練智能體執行複雜任務（例如導航、物體重新排列和遵循人類指令）的挑戰，同時提供評估其性能的工具，並允許人類與模擬環境進行互動。
How it works
該函式庫建立在 Habitat-Sim 核心模擬器之上，允許開發者：
- 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 (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 collect data or test trained agents.
Who it’s for
此專案是為從事具身智能、機器人模擬與自主智能體與室內環境互動研究的 AI 研究人員與開發者而設計的。
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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