Robothor Environment for RL training, abide `gym` API

Evaluation tasks for ObjectNav models

Code for the ICRA 2024 Cook2LTL paper on translating free-form cooking recipes to Linear Temporal Logic (LTL) formulae for robot task 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).

📱👉🏠 Perform conditional procedural generation to generate houses like your own!

🚀 Run AI2-THOR with Google Colab

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).

Visual Reaction: Learning to Play Catch with Your Drone

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

🔀 Visual Room Rearrangement

🏘️ Scaling Embodied AI by Procedurally Generating Interactive 3D Houses

CVPR 2024: Language Guided Generation of 3D Embodied AI Environments.