ai2thor-environment

There are 8 repositories under ai2thor-environment topic.

  • allenai/savn

    Learning to Learn how to Learn: Self-Adaptive Visual Navigation using Meta-Learning (https://arxiv.org/abs/1812.00971)

    Language:Python185133056

  • allenai/manipulathor

    ManipulaTHOR, a framework that facilitates visual manipulation of objects using a robotic arm

    Language:Jupyter Notebook90101213

  • TheMTank/cups-rl

    Customisable Unified Physical Simulations (CUPS) for Reinforcement Learning. Experiments run on the ai2thor environment (http://ai2thor.allenai.org/) e.g. using A3C, RainbowDQN and A3C_GA (Gated Attention multi-modal fusion) for Task-Oriented Language Grounding (tasks specified by natural language instructions) e.g. "Pick up the Cup or else"

    Language:Python488197

  • allenai/robustnav

    Evaluating pre-trained navigation agents under corruptions

    Language:Python27613
  • Safe-Deep-Learning-Based-Global-Path-Planning-Using-a-Fast-Collision-Free-Path-Generator

    our-projects-github/Safe-Deep-Learning-Based-Global-Path-Planning-Using-a-Fast-Collision-Free-Path-Generator

    Implementation of "Safe Deep Learning-Based Global Path Planning Using a Fast Collision-Free Path Generator". We present a global path planning method in this project which is based on an LSTM model that predicts safe paths for the desired start and goal points in an environment with polygonal obstacles, using a new loss function (MSE-NER).

    Language:Jupyter Notebook15103

  • SamsonYuBaiJian/actionet

    3D household task-based dataset created using customised AI2-THOR.

    Language:C#14314

  • erfan-ashtari/Path-planning

    Implementation of "Safe Deep Learning-Based Global Path Planning Using a Fast Collision-Free Path Generator". We present a global path planning method in this project which is based on an LSTM model that predicts safe paths for the desired start and goal points in an environment with polygonal obstacles, using a new loss function (MSE-NER).

    Language:Jupyter Notebook6101

  • shirin-chehelgami/Global-path-planning

    Implementation of "Safe Deep Learning-Based Global Path Planning Using a Fast Collision-Free Path Generator". We present a global path planning method in this project which is based on an LSTM model that predicts safe paths for the desired start and goal points in an environment with polygonal obstacles, using a new loss function (MSE-NER).

    Language:Jupyter Notebook1200