[
    {
        "Variant name": "main",
        "Reviewer name": "Julie Digne <julie.digne@liris.cnrs.fr>",
        "Is master variant (boolean)": true,
        "Is variant deprecated (boolean)": false,
        "Title": "Learning symmetric and low-energy locomotion",
        "DOI": "10.1145/3197517.3201397",
        "Year": 2018,
        "ACM Keywords": [
            "Learning paradigms",
            "Animation"
        ],
        "Topic {Rendering, Animation and Simulation, Geometry, Images, Virtual Reality, Fabrication}": "Animation and Simulation",
        "Co-authors from academia (boolean)": true,
        "Co-authors from industry (boolean)": false,
        "ACM Open Access (boolean)": false,
        "PDF on the authors' webpage / institution (boolean)": true,
        "PDF URL": "https://www.cc.gatech.edu/~turk/paper_pages/2018_symmetric_locomotion/symmetric_low_energy_locomotion.pdf",
        "PDF on Arxiv or any openarchive initiatives (boolean)": true,
        "Arxiv/OAI page URL": "https://arxiv.org/abs/1801.08093",
        "Project URL": "https://www.cc.gatech.edu/~turk/paper_pages/2018_symmetric_locomotion/",
        "Code available (boolean)": true,
        "If code not available, pseudo-code available (boolean)": false,
        "If pseudo-code, could the paper be trivially implemented? {0..4}": "",
        "Code URL": "https://github.com/VincentYu68/SymmetryCurriculumLocomotion",
        "Code URL2": "",
        "MD5 sum (for archives)": "",
        "git/hg/svn commit hash or revision number": "b50478f8eca673730e3ce1a5441b1948b31a5187",
        "MD5 sum (for archives) URL2": "",
        "git/hg/svn commit hash or revision number URL2": "",
        "Software Heritage permalink": "https://archive.softwareheritage.org/swh:1:dir:fa936fa9da4f75e51d2fb6aa97940c59166a48d1;origin=https://github.com/VincentYu68/SymmetryCurriculumLocomotion/",
        "Software type {Code, Binary, Partial Code}": "Code",
        "Code License (if any)": "MIT",
        "Are the code authors explicit? (boolean)": false,
        "Build/Configure mechanism": "Other script",
        "Dependencies": "libeigen3-dev libassimp-dev libccd-dev libfcl-dev libboost-regex-dev libboost-system-dev libopenscenegraph-dev libbullet-dev liburdfdom-dev libnlopt-dev libxi-dev libxmu-dev freeglut3-dev libtinyxml2-dev  swig tensorflow numpy dart pydart",
        "Does the software require paywall/proprietary software/material (boolean)?": false,
        "Does the code need data (not examples) (boolean)": false,
        "Nature of the data (pretrained model, LUT...)": "",
        "License of the data": "",
        "Able to perform a replicability test (boolean)": true,
        "If not able to perform a test, was it due to missing hardware/software? (boolean)": false,
        "Documentation score {0=NA,1,2,3}": 1,
        "Dependencies score {0=NA, 1,2,3,4,5}": 4,
        "Build/configure score {0=NA, 1,2,3,4,5}": 4,
        "Fixing bugs score (if any) {0=NA, 1,2,3,4,5}": 5,
        "Replicate paper results score {0=NA, 1,2,3,4,5}": 3,
        "Adaptability score to other contexts {0=NA, 1,2,3,4,5}": 2,
        "Time spent for the test (code download to first successful run, [0,10], 10min slots, 100min max)": 10,
        "Operating system for the test": "Linux",
        "Build instructions/comments": "All scripts are provided for reproducing the examples in the paper.\nThe dependencies can be easily installed using a script, but on ubuntu 19.10 I had to install libnlopt-cxx-dev.\nThe policy learning went well, but I was not able to produce the results of the paper, due to the failure of the test_policy function with error: Attempted to look up malformed environment ID: b'../data/precomp_data/dog_run1/policy_params.pkl'. (Currently all IDs must be of the form ^(?:[\\w:-]+\\/)?([\\w:.-]+)-v(\\d+)$.)\nThis occured also when testing the provided precomputed policies.\n\nI also tried with an older release of dart-env (v0.7.4 - 2017) which seems to be handling registration but the same problem occured.",
        "Misc. comments": "",
        "Software language": "Python"
    }
]