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alfworld avatar

alfworld/alfworld

0
View on GitHub↗
779 stars·93 forks·Python·MIT·12 views

Alfworld

Aligning Text and Embodied Environments for Interactive Learning Mohit Shridhar, Xingdi (Eric) Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, Matthew Hausknecht ICLR 2021

Features

  • Reasoning Environments - Text and embodied environment for interactive learning.
  • Simulation Environments - Environment aligning text-based tasks with embodied 3D scenes.
  • Visual Interactions - Aligns text-based environments with embodied interactive learning.
  • World Models - Framework for text-based embodied agents and world model interaction.

Star history

Star history chart for alfworld/alfworldStar history chart for alfworld/alfworld

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Alfworld

These projects share indexed features with Alfworld. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • allenai/manipulathorallenai avatar

    allenai/manipulathor

    99View on GitHub↗

    We present ManipulaTHOR , a framework that facilitates visual manipulation of objects using a robotic arm. Our framework is built upon a physics engine and enables realistic interactions with objects while navigating through scenes and performing tasks. Object manipulation is an established…

    Jupyter Notebook
    View on GitHub↗99
  • google/dopaminegoogle avatar

    google/dopamine

    10,879View on GitHub↗

    Dopamine is a reinforcement learning research framework designed for prototyping and testing algorithms across diverse simulated environments. It provides an agent development toolkit that utilizes a flat class hierarchy to facilitate the creation and extension of learning agents. The framework includes a standardization layer via environment wrappers that connect agents to various physics simulations and gaming environments. It also features a high-performance experience replay buffer for storing and sampling transition data to improve training stability, alongside a dedicated hyperparameter

    Jupyter Notebook
    View on GitHub↗10,879
  • facebookresearch/habitat-simfacebookresearch avatar

    facebookresearch/habitat-sim

    3,532View on GitHub↗

    Habitat-sim is a high-performance 3D simulation platform designed for training and benchmarking embodied AI agents within photorealistic indoor and outdoor environments. It serves as a simulator for AI and robotics, providing a system for generating synthetic data and simulating physical interactions. The project is distinguished by a native C++ core that enables high-throughput simulation and a rendering pipeline using physically based rendering and baked global illumination. It features a navigation system based on pre-computed navigation meshes to ensure collision-free traversal and a rigi

    C++aicomputer-visioncplusplus
    View on GitHub↗3,532
  • dusty-nv/jetson-inferencedusty-nv avatar

    dusty-nv/jetson-inference

    8,734View on GitHub↗

    jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

    C++caffecomputer-visiondeep-learning
    View on GitHub↗8,734
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Frequently asked questions

What does alfworld/alfworld do?

Aligning Text and Embodied Environments for Interactive Learning Mohit Shridhar, Xingdi (Eric) Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, Matthew Hausknecht ICLR 2021

What are the main features of alfworld/alfworld?

The main features of alfworld/alfworld are: Reasoning Environments, Simulation Environments, Visual Interactions, World Models.

Which projects share features with alfworld/alfworld?

Projects with overlapping indexed features include: allenai/manipulathor — We present ManipulaTHOR , a framework that facilitates visual manipulation of objects using a robotic arm. Our… google/dopamine — Dopamine is a reinforcement learning research framework designed for prototyping and testing algorithms across diverse… facebookresearch/habitat-sim — Habitat-sim is a high-performance 3D simulation platform designed for training and benchmarking embodied AI agents… deepmind/deepmind-research — This project is an AI research implementation library and machine learning research repository. It provides a… dusty-nv/jetson-inference — jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU… qwenlm/qwen2-vl — Qwen2-VL is a multimodal large language model and vision language model designed to process and reason across text,…