This is the official repository for the FLAIR algorithm and the paper "Getting Robots Back On Track: Reconstituting Control in Unexpected Situations with Online Learning".
Our system is based on ROS2 and docker-compose.
Run the following commands to build the docker container:
docker build -t airl:flair -f airl/Dockerfile .
docker build -t airl:ros2_light -f airl/Dockerfile.ros2_light .
docker build -t airl:zeds -f airl/Dockerfile.stereolabs .
Before running any part of the scripts, it is necessary to compile the ROS scripts via the build_messages.sh script.
cd fast_adaptation && ./build_messages.sh
Once this step is done, it is possible to start the full pipeline. The script to start our docker pipeline can be run via the start_flair.sh script.
./start_flair.sh
We run the docker-compose pipeline on our NVIDIA Jetson Orin 32Gb that is directly connected to a VectorNav V100 IMU and a Zed2 Camera in addition to the GVRBot. The docker-compose file that is executed can be found here.
Note Our setup requires the user to first start the ROS server on the GVRBot before running our pipeline (we note that the GVRBot runs ROS whereas our codebase is written in ROS2, but we provide a rosbridge as part of our pipeline that handles all the communication).
Finally, to launch the virtual driver, it is necessary to restart the container called vicon which will then listen to the VICON system sending out messages to the rostopic named after the tracked object. In our case this is the "/vicon/GVRBot_IC/GVRBot_IC" topic. (See File). In case you are running your the code on your own system, you need to adjust the topic names.
Run the command:
./restart_container.sh vicon
The main script implementing FLAIR is called adaptation.py. This script starts the multiple processes taking care of the data collection, processing and training. The code for each of these functionalities can be found in the functionality_controller folder which is imported into the adaptation.py script. The controller for the code can be found in the functionality_controller.py file, which is the code directly producing the commands.
All the parameters that can be tweaked can be found in a configuration file called adaptation_config.py. All the parameters listed here have been used for the experimental runs reported in our paper.
The script vicon.py contains the exact waypoints used for our experiments (which correspond to our lab settings), but they can easily be replaced by your path. At the beginning of the file, you can chose the perturbation you would like to apply to the robot. This file can be replaced by a manual controller for example if required.
In addition to the code for FLAIR, we publish our baselines under the files adaptation_rl.py and adaptation_lqr.py. Similarly, the files producing the commands at each step are called functionality_controller_rl.py and functionality_controller_lqr.py.
The script to start our docker pipeline for the baselines can be run via the rl_start.sh script.
./rl_start.sh
or
./lqr_start.sh
All the parameters that can be tweaked can be found in a configuration file called adaptation_config_rl.py or adaptation_config_lqr.py. All the parameters listed here have been used for the experimental runs reported in our paper.