Add sim_demo image

README.md

@@ -26,9 +26,14 @@
 
 <img align="right" width="40" height="40" src="https://github.com/leggedrobotics/wild_visual_navigation/blob/main/assets/images/dino.png" alt="Dino"> 
 
-## Installation
+## Setup
 
-### Minimal
+### Quick simulation demo
+We prepared a quick-start simulation with a Jackal robot using Docker. Please check the instructions [here](docker/README.md)
+
+![Overview](./assets/images/sim_demo.jpg)
+
+### Minimal setup
 Clone the WVN and our STEGO reimplementation.
 ```shell
 mkdir ~/git && cd ~/git 
@@ -73,8 +78,8 @@ pip3 install -e ./self_supervised_segmentation
  ┣ 📂wild_visual_navigation_msgs              # ROS1 message definitions
  ┣ 📂wild_visual_navigation_ros               # ROS1 nodes for running WVN 
     ┗ 📂scripts                               
-       ┗ 📜 wvn_feature_extractor_node.py     
-       ┗ 📜 wvn_learning_node.py    
+       ┗ 📜 wvn_feature_extractor_node.py     # Main process for feature extraction and inference
+       ┗ 📜 wvn_learning_node.py              # Main process that generates supervision signals and the online training loop
  ┗ 📜 quick_start.py                          # Inferencing demo_data from pre-trained checkpoints
 ```
 ### Features
@@ -87,7 +92,7 @@ pip3 install -e ./self_supervised_segmentation
 
 <img align="right" width="40" height="40" src="https://github.com/leggedrobotics/wild_visual_navigation/blob/main/assets/images/dino.png" alt="Dino"> 
 
-## Experiments
+## Demos
 
 ### Inference of pre-trained model
 
@@ -105,11 +110,11 @@ python3 quick_start.py
 ```
 
 ### Online adaptation [Simulation]
-Instructions can be found within [wild_visual_navigation_jackal/README.md](wild_visual_navigation_jackal/README.md).
+We prepared a Docker image to run a simulation with a Jackal robot, check the instructions [here](docker/README.md).
 
 ### Online adaptation [Rosbag]
-#### Download Rosbags:
-To quickly test out online training and adaption we provide some example rosbags ( [GDrive](https://drive.google.com/drive/folders/1Rf2TRPT6auFxOpnV9-ZfVMjmsvdsrSD3?usp=sharing) ), collected with our ANYmal D robot.
+#### Download rosbags:
+To quickly test out online training and adaption we provide some example rosbags ([GDrive](https://drive.google.com/drive/folders/1Rf2TRPT6auFxOpnV9-ZfVMjmsvdsrSD3?usp=sharing)), collected with our ANYmal D robot.
 
 #### Example Result:
 <div align="center">
@@ -121,7 +126,7 @@ To quickly test out online training and adaption we provide some example rosbags
 
 </div>
 
-#### ROS-Setup:
+#### ROS Setup:
 ```shell
 # Create new catkin workspace
 source /opt/ros/noetic/setup.bash
@@ -150,7 +155,7 @@ source /opt/ros/noetic/setup.bash
 source ~/catkin_ws/devel/setup.bash
 ```
 
-After successfully building the ros workspace, you can run the entire pipeline by either using the launch file or by running the nodes individually.
+After successfully building the ROS workspace, you can run the entire pipeline by either using the launch file or by running the nodes individually.
 Open multiple terminals and run the following commands:
 
 - Run wild_visual_navigation
@@ -184,13 +189,15 @@ python wild_visual_navigation_ros/scripts/wvn_learning_node.py
 - The general configuration files can be found under: `wild_visual_navigation/cfg/experiment_params.py`
 - This configuration is used in the `offline-model-training` and in the `online-ros` mode.
 - When running the `online-ros` mode, additional configurations for the individual nodes are defined in `wild_visual_navigation/cfg/ros_params.py`.
-- These configuration file is filled based on the rosparameter-server during runtime.
+- These configuration file is filled based on the ROS parameter server during runtime.
 - The default values for this configuration can be found under `wild_visual_navigation/wild_visual_navigation_ros/config/wild_visual_navigation`.
 
 
 <img align="right" width="40" height="40" src="https://github.com/leggedrobotics/wild_visual_navigation/blob/main/assets/images/dino.png" alt="Dino"> 
 
 ## Development
+<details>
+<summary>Here we provide additional details if you want to contribute.</summary>
 
 ### Install pre-commit
 ```shell
@@ -219,7 +226,7 @@ pytest
 ### Open-Sourcing
 Generating headers
 ```shell
-pip3 install adheader
+pip3 install addheader
 
 # If your are using zsh otherwise remove \
 addheader wild_visual_navigation -t header.txt -p \*.py --sep-len 79 --comment='#' --sep=' '
@@ -238,6 +245,7 @@ rosrun procman_ros sheriff -l ~/git/wild_visual_navigation/wild_visual_navigatio
 ```shell
 rosbag_play --tf --sem --flp --wvn  mission/*.bag
 ```
+</details>
 
 <img align="right" width="40" height="40" src="https://github.com/leggedrobotics/wild_visual_navigation/blob/main/assets/images/dino.png" alt="Dino"> 
 

docker/Dockerfile

@@ -123,4 +123,6 @@ RUN mkdir -p /root/catkin_ws/src \
 
 RUN echo "source /root/catkin_ws/devel/setup.bash" >> ~/.bashrc
 RUN echo "source /root/env/bin/activate" >> ~/.bashrc
+
+COPY docker/first_run.sh first_run.sh
 ENV DEBIAN_FRONTEND=dialog

docker/README.md

@@ -13,60 +13,64 @@ To build the container:
 docker compose -f docker-compose-gui-nvidia.yaml build
 ```
 
-## Run the container
+## Run the simulation environment in the container
 
-To run the container (terminal-based):
+Start the container in detached mode:
 
 ```sh
 docker compose -f docker-compose-gui-nvidia.yaml up -d
 ```
 
-To launch bash on the container:
+Launch a first bash terminal in the container to start the simulation environment:
 
 ```sh
-docker compose exec wvn bash
+docker compose -f docker-compose-gui-nvidia.yaml exec wvn_nvidia /bin/bash
 ```
 
-## Stop the container
+Once in the container, run the `first_run` script to install the WVN package that is mounted automatically when the container starts:
+```sh
+source first_run.sh
+```
 
-To stop the container:
+Launch the Gazebo simulation environment and an RViz window as the main interface.
 
 ```sh
-docker compose -f docker-compose.yaml stop
+roslaunch wild_visual_navigation_jackal sim.launch 
 ```
+If this doesn't open any window, pelase check the troubleshooting section below.
 
-## Running Wild Visual Navigation
 
-You can either run the following commands in 4 terminals that initialize a bash terminal in the container, or you can use VS Code with the Docker extension to instantiate terminal in the container directly.
-
-### Launch Jackal sim
+Open a new terminal to launch WVN in the container
 
 ```sh
-roslaunch wild_visual_navigation_jackal sim.launch 
+docker compose -f docker-compose-gui-nvidia.yaml exec wvn_nvidia /bin/bash
 ```
 
-### Launch WVN
-
+And then, once in the container:
 ```sh
 roslaunch wild_visual_navigation_jackal wild_visual_navigation.launch
 ```
 
-### Launch Teleop node
+You can drive the Jackal robot by sending 2D Nav goals using RViz. We implemented a simple [carrot follower](../wild_visual_navigation_jackal/scripts/carrot_follower.py) that was tuned for the demo (not actually used in real experiments)
 
-```sh
-roslaunch wild_visual_navigation_jackal teleop.launch 
-```
 
-### Launch RViz window
+## Stop the example
+
+Kill all the terminal as usual (Ctrl + D). Then, stop the container using:
 
 ```sh
-roslaunch wild_visual_navigation_jackal view.launch 
+docker compose -f docker-compose-gui-nvidia.yaml down
 ```
 
 
 ## Troubleshooting
 
-If the GUI doesn't work, you might need to allow the X Server to connect before running the container:
+If the GUI doesn't work, you'll see an error like:
+
+> No protocol specified
+> qt.qpa.xcb: could not connect to display :1
+
+To fix it, you might need to allow the X Server to connect before running the container:
 
 ```sh
 xhost +Local:*

docker/docker-compose-gui-nvidia.yaml

@@ -1,4 +1,3 @@
-version: "3.9"
 services:
   wvn_nvidia:
     extends:

docker/docker-compose-gui.yaml

@@ -1,4 +1,3 @@
-version: "3.9"
 services:
   wvn:
     extends:

docker/docker-compose-nvidia.yaml

@@ -1,4 +1,3 @@
-version: "3.9"
 services:
   wvn_nvidia:
     extends:

docker/docker-compose.yaml

@@ -1,4 +1,3 @@
-version: "3.9"
 services:
   wvn:
     build:
@@ -13,10 +12,4 @@ services:
       - ../../self_supervised_segmentation:/root/catkin_ws/src/self_supervised_segmentation
     environment:
       - ROS_IP=127.0.0.1
-    command: "/bin/bash"
-    # command: >
-    #   /bin/bash -c "pip3 install -e /root/catkin_ws/src/wild_visual_navigation
-    #           && pip3 install -e /root/catkin_ws/src/self_supervised_segmentation
-    #           && cd /root/catkin_ws && catkin build
-    #           && cd /root
-    #           && source /root/catkin_ws/devel/setup.bash"
+    command: "/bin/bash"

docker/first_run.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+
+echo "pip3 install -e /root/catkin_ws/src/self_supervised_segmentation ..."
+pip3 install -e /root/catkin_ws/src/self_supervised_segmentation > /dev/null
+
+echo "pip3 install -e /root/catkin_ws/src/wild_visual_navigation ..."
+pip3 install -e /root/catkin_ws/src/wild_visual_navigation > /dev/null
+
+echo "catkin build ..."
+catkin build > /dev/null
+
+echo "source devel/setup.bash ..."
+source devel/setup.bash > /dev/null
+
+echo "Setup ready!"

wild_visual_navigation_jackal/README.md

@@ -15,9 +15,7 @@ sudo apt-get update
 sudo apt update 
 sudo apt install -y \
         ros-noetic-jackal-simulator \
-        ros-noetic-jackal-desktop \
-        ros-noetic-teleop-twist-keyboard \
-        ros-noetic-rqt-robot-steering \
+        ros-noetic-jackal-desktop
 ```
 
 ## Running

wild_visual_navigation_jackal/launch/sim.launch

@@ -34,4 +34,7 @@
   <node name="gazebo_world_publisher" pkg="wild_visual_navigation_jackal" type="gazebo_world_publisher.py"/>
   <node name="jackal_carrot_follower" pkg="wild_visual_navigation_jackal" type="carrot_follower.py"/>
 
+  <!-- RViz window -->
+  <include file="$(find wild_visual_navigation_jackal)/launch/view.launch"/>
+
 </launch>

wild_visual_navigation_jackal/scripts/carrot_follower.py

@@ -23,7 +23,7 @@
 MAX_LINEAR_VEL = 0.5
 MAX_ANGULAR_VEL = 0.7
 GAIN_LINEAR = 1.0
-GAIN_ANGULAR = 1.0
+GAIN_ANGULAR = 1.5
 DIST_THR = 0.1  # 10 cms
 
 

wild_visual_navigation_jackal/scripts/gazebo_world_publisher.py

@@ -20,6 +20,9 @@
 import tf.transformations as tr
 
 
+last_stamp = None
+
+
 def msg_to_se3(msg):
     """Conversion from geometric ROS messages into SE(3)
     Based on Jarvis Schultz's: https://answers.ros.org/question/332407/transformstamped-to-transformation-matrix-python/
@@ -54,7 +57,11 @@ def msg_to_se3(msg):
 
 
 def gazebo_callback(msg):
+    global last_stamp
     stamp = rospy.Time.now()
+    if stamp == last_stamp:
+        return
+
     T_world_base = msg_to_se3(msg.pose[1])  # this is the base_link pose in world frame (from gazebo)
     T_base_world = np.linalg.inv(T_world_base)
 
@@ -74,10 +81,12 @@ def gazebo_callback(msg):
     br.sendTransform(t)
 
     pub.publish(marker)
+    last_stamp = stamp
 
 
 if __name__ == "__main__":
     rospy.init_node("gazebo_world_publisher")
+    last_stamp = rospy.Time.now()
 
     # Default variables
     rospack = rospkg.RosPack()