Skip to main content

Humanoid Standing Up

Project description

HoST - Pytorch (wip)

Implementation of Humanoid Standing Up, from the paper Learning Humanoid Standing-up Control across Diverse Postures out of Shanghai, in Pytorch

Besides for the set of reward functions, the other contribution is validating an approach using multiple critics out of Boston University

Citations

@article{huang2025host,
  title     = {Learning Humanoid Standing-up Control across Diverse Postures},
  author    = {Huang, Tao and Ren, Junli and Wang, Huayi and Wang, Zirui and Ben, Qingwei and Wen, Muning and Chen, Xiao and Li, Jianan and Pang, Jiangmiao},
  journal   = {arXiv preprint arXiv:2502.08378},
  year      = {2025},
}
@article{Farebrother2024StopRT,
    title   = {Stop Regressing: Training Value Functions via Classification for Scalable Deep RL},
    author  = {Jesse Farebrother and Jordi Orbay and Quan Ho Vuong and Adrien Ali Taiga and Yevgen Chebotar and Ted Xiao and Alex Irpan and Sergey Levine and Pablo Samuel Castro and Aleksandra Faust and Aviral Kumar and Rishabh Agarwal},
    journal = {ArXiv},
    year   = {2024},
    volume = {abs/2403.03950},
    url    = {https://api.semanticscholar.org/CorpusID:268253088}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

host_pytorch-0.0.5.tar.gz (606.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

host_pytorch-0.0.5-py3-none-any.whl (3.4 kB view details)

Uploaded Python 3

File details

Details for the file host_pytorch-0.0.5.tar.gz.

File metadata

  • Download URL: host_pytorch-0.0.5.tar.gz
  • Upload date:
  • Size: 606.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.21

File hashes

Hashes for host_pytorch-0.0.5.tar.gz
Algorithm Hash digest
SHA256 b61d32835ca248582ad2544448a2dcadd3c4e1ba7bd464643a3c77d495702879
MD5 d2643ed9475310e08163781d30284882
BLAKE2b-256 552ef16e32a7b3544ec2bd970bb94de6af9794f03add148a817acd4f989d869f

See more details on using hashes here.

File details

Details for the file host_pytorch-0.0.5-py3-none-any.whl.

File metadata

  • Download URL: host_pytorch-0.0.5-py3-none-any.whl
  • Upload date:
  • Size: 3.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.21

File hashes

Hashes for host_pytorch-0.0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 833642503cf13c8924383e27be7beeb83ccf6ed4e0ffd8b6b5f2c3473b62adc6
MD5 93c5fe80641e2686b67f7c1db7d5d08f
BLAKE2b-256 6e4a9cd821959fab648f9f08856a07469e07fc7047f270b06924c8ff21ace6ea

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page