https://github.com/ami-iit/paper_ramadoss_2022_humanoids_human-base-estimation

[Humanoids 2022] https://ieeexplore.ieee.org/abstract/document/10000199

https://github.com/ami-iit/paper_ramadoss_2022_humanoids_human-base-estimation

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 1 DOI reference(s) in README
  • Academic publication links
    Links to: ieee.org
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (11.8%) to scientific vocabulary
Last synced: 7 months ago · JSON representation

Repository

[Humanoids 2022] https://ieeexplore.ieee.org/abstract/document/10000199

Basic Info
  • Host: GitHub
  • Owner: ami-iit
  • License: bsd-3-clause
  • Language: Dockerfile
  • Default Branch: main
  • Homepage:
  • Size: 2.69 MB
Statistics
  • Stars: 6
  • Watchers: 5
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Created over 3 years ago · Last pushed about 3 years ago
Metadata Files
Readme License

README.md

Estimation of Human Base Kinematics using Dynamical Inverse Kinematics and Contact-Aided Lie Group Kalman Filter

P. Ramadoss, L.Rapetti, Y.Tirupachuri, R.Grieco, G. Milani, E. Valli, S. Dafarra, S. Traversaro and D. Pucci "Estimation of Human Base Kinematics using Dynamical Inverse Kinematics and Contact-Aided Lie Group Kalman Filter" in IEEE 2022 International Conference on Humanoid Robots

ICHR 2022 - Estimation of Human Base Kinematics

2022 International Conference on Humanoid Robotics (Humanoids 2022)
Installation | Document | YouTube

Reproducing the experiments

We provide a containerised virtual environment using Docker and Conda in order to launch the software in an isolated, reproducible manner. The dependencies and related versions used to build the environment can be checked in the deps folder. However, one can simply pull the pre-built docker image from the GitHub registry, since a docker.yaml in the .github/workflows folder dispatches a build workflow for the docker image.

  1. Pull the docker image: bash docker pull ghcr.io/ami-iit/human-base-estimation-docker:latest

  2. Launch the container: bash xhost + docker run -it --net=host --env="DISPLAY=$DISPLAY" --volume="/tmp/.X11-unix:/tmp/.X11-unix" ghcr.io/ami-iit/human-base-estimation-docker:latest

Please be aware that xhost + is not a safe way to expose the X Server running on the host machine to the docker container. But in this scenario, we are not doing anything undesirable, so it is acceptable.

  1. The experiment will start by automatically launching a yarp server, loading the dataset and launching the necessary applications along with the visualizer. The experiment will end and everything will close automatically.

For more details on the installation, implementation, and parameters configuration. please check KinDynFusion repository.

Known issues: Starting the docker daemon using Docker Desktop does not allow to display the visualizer GUI on the screen. I had to start the Docker daemon using dockerd with root privileges and then run the docker container also with root privileges in order to visualize the experiment.

Citing this work

@INPROCEEDINGS{10000199, author={Ramadoss, Prashanth and Rapetti, Lorenzo and Tirupachuri, Yeshasvi and Grieco, Riccardo and Milani, Gianluca and Valli, Enrico and Dafarra, Stefano and Traversaro, Silvio and Pucci, Daniele}, booktitle={2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)}, title={Estimation of Human Base Kinematics using Dynamical Inverse Kinematics and Contact-Aided Lie Group Kalman Filter}, year={2022}, volume={}, number={}, pages={364-369}, doi={10.1109/Humanoids53995.2022.10000199}}

Maintainer

This repository is maintained by:

| | | | :----------------------------------------------------------: | :--------------------------------------------------: | | | @prashanthr05 |

Owner

  • Name: Artificial and Mechanical Intelligence
  • Login: ami-iit
  • Kind: organization
  • Location: Italy

GitHub Events

Total
Last Year

Committers

Last synced: 10 months ago

All Time
  • Total Commits: 16
  • Total Committers: 3
  • Avg Commits per committer: 5.333
  • Development Distribution Score (DDS): 0.188
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Prashanth p****s@g****m 13
Lorenzo Rapetti l****i@g****m 2
Daniele Pucci d****5@g****m 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 1
  • Total pull requests: 3
  • Average time to close issues: N/A
  • Average time to close pull requests: about 17 hours
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 6.0
  • Average comments per pull request: 2.0
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • lrapetti (1)
Pull Request Authors
  • prashanthr05 (2)
  • lrapetti (1)
Top Labels
Issue Labels
Pull Request Labels

Dependencies

.github/workflows/docker.yaml actions
  • actions/checkout main composite
  • elgohr/Publish-Docker-Github-Action main composite
Dockerfile docker
  • ubuntu 22.04 build