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Simulation, benchmark, and dataset for active uncertainty-driven visuo-haptic shape completion.

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Active Visuo-Haptic Object Shape Completion

This repository includes code for Active Visuo-Haptic Object Shape Completion. It provides a simulation environment, a dataset (including data from the real robot), and can serve as a benchmark / baseline if you want to compare with performance we achieved. Authors: Lukas Rustler, Jens Lundell, Jan Behrens, Ville Kyrki and Matej Hoffmann
Maintainer: Lukas Rustler, [email protected]

Check out youtube video as well

Contents

Licensing

This repository uses various repositories. See LICENSE and the original repositories for licensing info:

Installation

This repository is tested on Ubuntu 18.04 with ROS Melodic. With minor adjustments, it will run on ROS Kinetic (some functions regarding mainly planning will need to be manually added), but it will not run on other versions of ROS. It also needs UNIX based system, i.e., it will not work on Windows.

Docker

Installation of all dependencies may be troublesome, and we recommend using the provided Docker image.

  • Install Docker from https://docs.docker.com/engine/install/
  • Install nvidia-docker from https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker
    • for better utilization of the gpu
  • Pull the image docker pull rustlluk/visuo-haptic-shape-completion:latest
  • Alternatively you can build your own image from Dockerfile
  • Optionally, you can call docker tag rustlluk/visuo-haptic-shape-completion:latest <image_name>, where <image_name> can be any name you want the image to have, to be more user-friendly for you
  • Other user defined name is <container_name>. It is used by other Docker commands, e.g., docker start <container_name> && docker attach <container_name> will run the container after you first do docker run (described later)

The are two possible (recommended) ways to link data from your computer:

  1. Link only data
    • Run docker run -it -p 6080:6080 --name <container_name> -v path_to_data_folder_on_your_pc:/root/act_vh_ws/src/shape_completinon/data --gpus all <image_name> /bin/bash, where <container_name> is name of the container selected by you and <image_name> is either rustlluk/visuo-haptic-shape-completion:latest or a name selected by you with docker tag
    • This will link a data folder from your computer to the container and all data created in visuo-haptic experiments will be stored directly in your pc
    • All code is stored inside the container and you can edit it in inside the contained (there are some editors installed and you can install your prefered one)
      • Be careful with PyCharm editor as it seems to kill VNC session. But maybe another version than we used may be working fine
    • To save changes made in the container, you just need to exit the container (CTRL+D or exit) and run docker commit <container_name> <image_name>
  2. Link the whole repository
    • Get to some folder cd path_to_the_folder and clone this repository git clone https://github.com/ctu-vras/visuo-haptic-shape-completion.git
    • run docker run -it -p 6080:6080 --name <container_name> -v path_to_the_folder/visuo-haptic-shape-completion:/root/act_vh_ws/src/ --gpus all <image_name> /bin/bash
      • <container_name> and <image_name> have the same function as in Option 1.
    • This way, all the code will be linked from your computer to the container. You can edit it on your computer, and all changes will be immediately transferred to the container
    • The repository is already built inside the container, and all should work immediately. However, if you occur some errors (or if you change some C++ files), you can rebuild the repository inside the container. Easilly run cd /root/act_vh_ws and catkin build
    • With this methods, you will need to download meshes from https://drive.google.com/file/d/1KKg9DpzX_2IhxWZsIzGgwEh5kDoz6eCq/view?usp=sharing and put them into kinova_mujoco/GT_meshes
  • After you docker run with either method you need to run start-vnc-session.sh
    • it will run noVNC session inside the container
    • the vnc is needed for OpenGL software
  • On your computer, open your favorite internet browser and connect to localhost:6080
    • you can run the container on other computer and connect to computer_ip:6080 or computer_hostname:6080
VNC Desktop Environment RVIZ running in VNC
vnc_desktop vnc_rviz

Tips and Tricks

  • You can work entirely in the container over VNC---it contains a desktop environment with an internet browser, file manager etc. However, we usually use the container just as a screen and do everything in terminals on the host computer
    • you can open another terminal in the container with docker exex -it <container_name>
  • Unfortunately, you can not copy-paste from the host computer to the container.
  • If you have only one screen and do not want to work inside the container, you can open it on a mobile phone just by connecting to the VNC in your phone's internet browser
  • You can delete the container with docker container rm <container_name>
    • or delete all Docker resources with docker system prune -a
  • It may happen that VNC stop to work. Just rerun start-vnc-session.sh again
  • If you need sudo for every Docker command, get to the end of the installation notes in https://docs.docker.com/engine/install/
  • GPIS reconstruction does not work in the Docker container by default because Matlab is needed, and we can not install Matlab without a license. You can install Matlab in the container by yourself (and matlab_engine for Python) to use GPIS.

Manual installation

If you have problems with some dependencies, check Dockerfile where we installed everything

  • Setup a ROS workspace and clone this repository into src
  • Install both python2 and python3
  • Check dependencies for all repositories mentioned in Licensing
  • We recommend to use Python environments. However, if you want to use them properly, you have to change some lines in the code:
    • Add lines to you ~/.bashrc
      • for example alias shape_rec_p3='source your_python3_virtual_environment/bin/activate && source your_catkin_ws/devel/setup.bash'
        • and the same for python2
      • Add /bin/bash -i -c shape_rec_p3 && to the start of lines 187 and 208 in main.py and lines 409 and 428 in evaluation_utils.py
      • Add /bin/bash -i -c shape_rec_p2 && to the start of lines 122, 125 and 145 in do_experiments.py and line 466 in evaluation_utils.py
  • Install required libraries which may not be included in the previous
    • pip3 install trimesh open3d pymeshfix
    • pip2 install trimesh open3d scikit-learn scipy
    • also Python-PCL and binvox_rw are needed
    • If there is any library needed and not mentioned above, installation through pip should be enough
  • build the workspace
  • cd your_catkin_ws && catkin build
  • add source your_catkin_ws/devel/setup.bash to your ~/.bashrc file
  • V-HACD and Fast-Robust-ICP contain built binaries and will work on most systems (with Ubuntu 18.04), but it may be required to be rebuild
    • however, the pipeline expects this binaries to be in system path, so you have to add them, e.g.,
      • ln -s your_catkin_ws/src/v-hacd/build/linux2/test/testVHACD /usr/bin/testVHACD
      • ln -s your_catkin_ws/src/Fast-Robust-ICP/build/FRICP /usr/bin/FRICP
    • In some cases change of permission will be needed as well
      • (sudo) chmod +x your_catkin_ws/src/Fast-Robust-ICP/build/FRICP
    • the same applies for run_simulation.sh
      • (sudo) chmod +x your_catkin_ws/src/shape_completion/scripts/run_simulation.sh
      • ln -s your_catkin_ws/src/shape_completion/scripts/run_simulation.sh /usr/bin/run_simulation
    • install MuJoCo to ~/.mujoco
      • We expect MuJoCo 2.10 to be installed. If you have other version, change Findmujoco.cmake file in the mujoco-ros package

Data

Repeating our experiments

With these commands, you can repeat our experiments (or run your own just by changing the experiments file). We are using paths in the Docker environment, so if you installed the pipeline manually, change the paths accordingly. More details on each command are written later.

  • Run the experiments with rosrun shape_completion do_experiments.py -s /root/act_vh_ws/src/shape_completinon/data/exps/simulation.json
    • this will run the simulation and make three repetitions with five touches on the whole dataset
    • if you want to use other objects (or number of repetitions and touches), change the simulation.json file
  • After the experiments are done, check /root/act_vh_ws/src/shape_completion/data/logs and locate the newest .log file. It will be named with a timestamp. For easier use, you can rename it. We will refer it as simulation.log.
  • Evaluate the results with rosrun shape_completion evaluation_utils.py graphs_from_logs simulation.log
    • this will create graphs in /root/act_vh_ws/src/shape_completion/data/figures and .npz files in /root/act_vh_ws/src/shape_completion/data/offline_nn_vals
  • [Optionally] Reconstruct with baseline methods with rosrun shape_completion baselines.py reconstruct_from_logs simulation.log
    • New meshes will be created in /root/act_vh_ws/src/shape_completion/data/meshes. Graphs and .npz files will be created as well
  • [Optionally] If you want meshes of a very different objects, you may need to retrain the network as described in Training the network

Training the network

If you want to train your own model instead of using our pretrained model, follow the instructions:

  • prepare the meshes you want to train on in .ply format
  • prepare split file similar to no_ycb.json
    • the structure is dictionary with key named as name of the dataset and value is another dictionary
    • the nested dictionary includes name of the object (without suffix) as key and array with string values denoting which rotations to use
      • E.g., "0_z"
      • only the possibilities in no_ycb.json are available, if you want other rotations, you have to change grasp_train.py
  • prepare training setup as in shape_completion_setup.conf
  • cd path_to_the_ws/IGR/code
  • python3 preprocess/grasp_train.py -p path_to_folder_with_ply_meshes (can be kinova_mujoco/GT_meshes) -s path_to_your_split_file -c number_of_CPU_cores to use
    • this will create the training point clouds saved in IGR/data/npy
  • python3 shapespace/train.py --expname your_desired_experiment_name --gpu number_of_the_gpu (0 if only one is available) --conf name_of_your_traininig_setup (relatively to shapespace directory) --split path_to_split_file (relatively to splits directory) --batch_size batch_size_value (we used 8)

Running the simulation

  • Option 1 (recommended):
    • run run_simulation.sh script with arguments:
      • name of .stl file (without .stl suffix) in meshes folder which will be loaded into the simulation
      • list [x,y,z] (Without spaces after commas) of coordinates where the object is placed
      • true/false whether to use 3D printed finger (True recommend)
      • true/false whether to use convex decomposition of the objects (True recommend)
  • Option 2:
    • Run rosrun kinova_mujoco prepare_object.py -n name_of_the_object -o origin_of_the_object -f use_printed_finger -c use_convex_decomposition, where the parameters are the same as in Option 1
    • Run roslaunch kinova_mujoco table_simulation_generated.launch object_name:=name_of_the_object object_origin:="origin_of_the_object" printed_finger:="bool" convex_decomp:="bool", where all parameters are the same as above
  • then run rosrun shape_completion main.py, with possible parameters:
    • -r - number of reconstructions (without the first one)
    • -t - number of touches between each reconstruction
    • -d - name of the collision detection type (cusum, threshold)

Experiments

One can run the experiments more easier by creating .json setup files (see setups for examples) with fields:

  • objects - list of string names of objects to use
  • repetitions - list of ints of how many times to repeat each experiment
  • reconstructions - list of ints of how many reconstructions to make (without the first one)
  • touches - list of ints of how many touches to do between reconstructions

Then the pipeline can be run with rosrun shape_completion do_experiments.py -s path_to_setup

  • with optinal parameters:
    • -l, --log_folder - specify where to save logs; default is to save next to the setups
    • -d, --detection_type - specify which method to use for collision detection - cusum, threshold; default: cusum
    • -i, --interactive - specify whether it should be run in interactive mode, i.e., user can decide whether everything is loaded fine; important only for the real setup; default: False
    • -r, --real_setup - specify whether we want to run real setup; default: False

RVIZ vizualizations

  • RVIZ can show these:
    • Robot planning group
      • used for planning in RVIZ
    • Robot visualization
      • manipulator, table and object on the table
    • image from the virtual camera
    • point cloud from the virtual camera
    • segmented point cloud from the virtual camera
    • TF names
    • Reconstructed mesh
    • Direction arrow
    • Point cloud with new information

Image from the virtual camera

Segmented point cloud in RVIZ (red)

New information added to point cloud in RVIZ (red)

Current reconstruction and direction arrow in RVIZ (both red)

Evaluation

  • evaluation of results can be done through evaluation_utils.py.
  • One would like to run:
    • rosrun shape_completion evaluation_utils.py grasphs_from_logs log_name, where log_name is name of the log with the experiment. This command runs basic graphs and creates .npz files with Chamfer distance and Jaccard similarity
    • rosrun shape_completion evaluation_utils.py run_all, which runs all the possible graphs.
  • the results are saved in .npz files. It can be loaded as numpy.load(file_name, allow_pickle=True)
    • it contains four keys: chamfer_all, chamfer_total, jaccard_all, jaccard_total
    • however, only the *_all are interesting
    • each key contains another dictionary, where keys are names of the objects. Values are other dictionaries with keys named after experiments, e.g., "5,1,3" is experiment with five touches, 1 reconstruction between touches and 3 repetitions. Each key contains python array of size Rx(T+1), where R is number of repetitions and T is number of touches.
      • For example, values of Jaccard similarity for drill box with "5,1,3" experiment can accessed like this: d['jaccard_all'][()]["drill_box"]["5,1,3"]

Baselines

Baseline reconstruction methods can be used from baselines.py. One can run rosrun shape_completion baselines.py reconstruct_from_logs log_name.log to reconstruct with all the methods.

Real setup

This repository does not include drivers for our robot, gripper and camera. But we still keep the possibility of the real setup in the code. You can get inspired from real.launch, where we commented places where you need to put your drivers. The rest of the code contains parameter real_setup, which control behaviour of some functions. Also our robot had joints names as joint_0 etc., you will need to change line 108 in main.py to your names. Running of the setup can be then done like this (or with experiments runner described in Repeating our experiments and Experiments running):

  • Run roslaunch shape_completion real.launch, with the same arguments as for the simulation (withou convex_decomp)
    • check if everything runs fine -- the cameras sometimes do not initialize properly and restart of the launch is needed
  • Run rosrun shape_completion main.py, with the same arguments as for the simulation

Publication

This repository is connected to the following publication:

Rustler, L., Lundell, J., Behrens, J. K., Kyrki, V., & Hoffmann, M. (2022). 'Active Visuo-Haptic Object Shape Completion'. IEEE Robotics and Automation Letters 7 (2), 5254-5261. [DOI IEEE Xplore][arxiv][youtube-video]

If you use the code or data in this repository, please cite this article.

Acknowledgements

This work was supported by the project Interactive Perception-Action-Learning for Modelling Objects (IPALM, https://sites.google.com/view/ipalm) (H2020 - FET - ERA-NET Cofund - CHIST-ERA III / Technology Agency of the Czech Republic, EPSILON, no. TH05020001).

We also thank Jan Behrens for contributions to the simulation environment through the following repositories: MuJoCo-ROS and Kinova_Mujoco.
Last but not least, we thank Robotology community for help the with Docker image.

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Simulation, benchmark, and dataset for active uncertainty-driven visuo-haptic shape completion.

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