Search Mode: Search through the graph for a node with a description that has high semantic similarity to the goal. If such a node is found and the similarity is above a threshold, the robot will move directly to that node. Exploration Mode: If no node with high enough semantic similarity is found, the robot will continue to explore by expanding nodes and assessing their scores and descriptions relative to the goal.

• src/: Contains the source code of Reasoned_Explorer
  • utils/: Helper functions.
  • simulator_utils/: Helper functions for Airsim
  • LLM_functions: All llm functions goes here
  • LLM_functions_async: Paralleled LLM functions
  • simulate: Main script for experiments in simulator, where you specify the goal and the algorithm to run
  • real: An example script for experiments in real environment, need additional local planners/GPS implementation for your own robot setup.
  • exploration_simulator : The logic script for graph building and visualization
  • RRT: The main agent script for Reasoned-Explorer, where hullucination and action happens
  • VLM: Kosmos-2 VLM can be queried on our server
  • settings.json: Default settings for AirSim's sensors, you could add cameras and add other sensors here.

1. Install requirements.txt

2. Setup:

   • Setup Google API-key
   • Setup OpenAI API-key

3. Add OpenAI API-Key to environment variable:

   • nano ~/.bashrc
   • export OPENAI_API_KEY="your_api_key_here"
   • source ~/.bashrc
   • conda activate reasoned #reactivate conda environment
   • echo $OPENAI_API_KEY #Test if your API keys is added correctly

4. Example Use: python run.py --exp_name "Forest Exploration" --type RRT --goal "Find the river" --branches 2 --rounds 3

1. Install habitat follows conda install cmake=3.14.0 conda install habitat-sim withbullet -c conda-forge -c aihabitat git clone --branch stable https://github.com/facebookresearch/habitat-lab.git cd habitat-lab pip install -e .

1. Activate conda environment

If you find our work useful in your research, please consider citing our paper. Below is the BibTeX entry:

@misc{xie2023reasoning,
      title={Reasoning about the Unseen for Efficient Outdoor Object Navigation}, 
      author={Quanting Xie and Tianyi Zhang and Kedi Xu and Matthew Johnson-Roberson and Yonatan Bisk},
      year={2023},
      eprint={2309.10103},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}