Can an Embodied Agent Find Your “Cat-shaped Mug”? LLM-Based Zero-Shot Object Navigation

This repo contains code for our paper -

Can an Embodied Agent Find Your “Cat-shaped Mug”? LLM-Based Zero-Shot Object Navigation.

Vishnu Sashank Dorbala, James F. Mullen Jr., Dinesh Manocha

Overview

Abstract

We present LGX (Language-guided Exploration), a novel algorithm for Language-Driven Zero-Shot Object Goal Navigation (L-ZSON), where an embodied agent navigates to an uniquely described target object in a previously unseen envi- ronment. Our approach makes use of Large Language Models (LLMs) for this task by leveraging the LLM’s commonsense- reasoning capabilities for making sequential navigational deci- sions. Simultaneously, we perform generalized target object de- tection using a pre-trained Vision-Language grounding model. We achieve state-of-the-art zero-shot object navigation results on RoboTHOR with a success rate (SR) improvement of over 27% over the current baseline of the OWL-ViT CLIP on Wheels (OWL CoW). Furthermore, we study the usage of LLMs for robot navigation and present an analysis of various prompting strategies affecting the model output. Finally, we showcase the benefits of our approach via real-world experiments that indicate the superior performance of LGX in detecting and navigating to visually unique objects.

Prerequisites

Our experiments use Python 3.8, ROS Melodic, CUDA 11.1 and Torch 1.10.0. In addition, you will need to install the following packages/submodules.

1. Conda Setup - Use the conda_reqs.txt file provided to setup the conda environment for LGX.
2. GLIP - Follow the installation instructions from the official documentation. Make sure to include the GLIP folder inside the root directory. GLIP_utils.py contains helper funtions for using and running GLIP.
3. OpenAI GPT-3 API Key - You need to update the OPENAI_KEY parameter in helper.py with your key.
4. A ROS Navigation Stack and RTABMAP - We use RTABMAP along with the husky navigation package for generating a occupancy grid and path planning. The former can be installed using instructions here, and the latter using sudo apt install ros-melodic-husky-navigation. Once installed, replace the costmap_common.yaml file in the configs folder of the package with the one provided in this repo.
5. YOLO and LAVIS Finally, we also require LAVIS and YOLO to be installed for running either LAVIS + LGX or YOLO + LGX.

Installation

To run LGX, we use ROS melodic with python 3.8. This requires you to first need to setup a catkin workspace as follows.

Catkin Workspace Setup

1. You'll need to install ROS. Since python3 isn't supported in Melodic, some packages need to be catkin installed to handle opencv and tf2_ros errors.
2. Catkin install - geometry2, vision-opencv, and source the workspace. Use GCC version 9 and G++ version 9.
3. Use catkin config --extend /opt/ros/melodic to set your $CMAKE_PREFIX_PATH in the catkin workspace (More details here).
4. Setup catkin build, and use the config - -DPYTHON_EXECUTABLE=/python3.8-install-location/bin/python -DPYTHON_INCLUDE_DIR=/python3.8-install-location/include/python3.8 -DPYTHON_LIBRARY=/python3.8-install-location/lib/libpython3.8.so. Replace the executable paths with wherever python3.8 has been installed in your system. It can point to your conda environment.
5. Follow instructions from here to install the geometry2 package for python3. The instructions use catkin_make, but it can simply be substituted with catkin build which is far more convenient to use.
6. Then download the melodic/noetic/yourversion branch of the vision-opencv package (here) in the catkin src folder, and build that using catkin build.
7. Finally, after building everything, ensure that your catkin workspace is sourced in the .bashrc file AFTER the ROS source. Echoing the $CMAKE_PREFIX_PATH should look something like - /home/user/catkin_ws/devel:/opt/ros/melodic.
8. If all these steps have been followed correctly, after opening up a python3.8 terminal, running import tf2_ros and from cv_bridge.boost.cv_bridge_boost import getCvType should return no errors.

Inference & Results

1. Run roscore, and keep it running on a separate terminal.
2. Activate the conda environment using conda activate LGX.
3. Run roslaunch move.launch, followed by python3 lgx-run.py in conda terminals.
4. (Optional) Open rviz, and load the lgx.rviz config file provided for visualization. Or run rviz -d lgx.rviz.
5. Once the experiment is done, you can get statistics and results using compute_results.py.

If everything runs successfully, you should see the results folder getting populated, and rviz showing the map and pointcloud similar to the image below.

Useful Commands, Tips & Debugging Info

• The presence of a results folder (empty or not) is necessary for the code to run. Clear this folder if you need to start afresh.
• Use rosservice call /rtabmap/reset to clear the costmap and pointcloud.
• Run rosrun map_server map_saver [--occ <threshold_occupied>] [--free <threshold_free>] [-f <mapname>] map:=/your/costmap/topic for manually saving the map.
• If you get CUDA Runtime errors, run nvidia-smi, and note the PID for the python3 process(es). Then kill it using sudo kill -9 PID.
• Ensuring the gridmap size is the same in costmap_common.yaml and Thor's locobot controller is very important. Right now it is set to 0.25. If you change this value, make sure you change it in both places.
• Check rosparam get /move_base/NavfnROS/allow_unknown is False to ensure the path planning takes place only inside the visible costmap, when target locking starts taking place. Also ensure rosparam get /move_base/global_costmap/track_unknown_space returns True.
• If you face cuda toolkit sourcing errors, add the CUDA toolkit install location to .bashrc as follows- export CUDA_HOME="/cuda_install_path/cuda" export PATH=/cuda_install_path/cuda/bin:$PATH export LD_LIBRARY_PATH="/cuda_install_path/cuda/lib64:$LD_LIBRARY_PATH"
• We use the following command for a Torch + CUDA install (CUDA 11.1, torch v1.10.0) pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
• If you get the Libtiff error, remove only that package from conda -conda remove --force libtiff
• I've used notify.run for debugging at various points in the code (commented). You can try to set it up yourself incase you face issues.
• Run TMPDIR=<cache_dir> pip install --cache-dir=<cache_dir> pkg_name if running low on space during pip install.

If you found our work useful, please do cite us!

@article{dorbala2023can,
  title={Can an Embodied Agent Find Your" Cat-shaped Mug"? LLM-Based Zero-Shot Object Navigation},
  author={Dorbala, Vishnu Sashank and Mullen Jr, James F and Manocha, Dinesh},
  journal={arXiv preprint arXiv:2303.03480},
  year={2023}
}