HoST-pytorch

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

Install

$ pip install HoST-pytorch

Usage

import torch
from host_pytorch import Agent
from host_pytorch.mock_env import Env, mock_hparams

env = Env()

agent = Agent(
    num_actions = (10, 10, 20),
    actor = dict(
        dims = (env.dim_state, 256, 128),
    ),
    critics = dict(
        dims = (env.dim_state, 256),
    ),
    reward_hparams = mock_hparams()
)

memories = agent(env)

agent.learn(memories)

agent.save('./standing-up-policy.pt', overwrite = True)

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}
}

@article{Tao2022LearningTG,
    title  = {Learning to Get Up},
    author = {Tianxin Tao and Matthew Wilson and Ruiyu Gou and Michiel van de Panne},
    journal = {ACM SIGGRAPH 2022 Conference Proceedings},
    year   = {2022},
    url    = {https://api.semanticscholar.org/CorpusID:248496244}
}

Metadata

Release files for HoST-pytorch 0.0.50

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for HoST-pytorch 0.0.50
File	Size	Uploaded
host_pytorch-0.0.50.tar.gz	4.5 kB	Aug 5, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for HoST-pytorch 0.0.50
File	Interpreter	ABI	Platform	Reset
host_pytorch-0.0.50-py3-none-any.whl 3.8 kB Aug 5, 2026	Python 3	none	any	Details

Total release size: 8.3 kB

Release files / host_pytorch-0.0.50.tar.gz

Download URL	host_pytorch-0.0.50.tar.gz
Size	4.5 kB
Tags	Source
SHA-256 checksum How to use checksums	`37b18a048e8401c6c736d3d6aa11dd14ee9ad453f4b78f93210dea0b18ac1b3e`
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Upload date	Aug 5, 2026
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Uploaded via	`uv/0.8.17`

Release files / host_pytorch-0.0.50-py3-none-any.whl

Download URL	host_pytorch-0.0.50-py3-none-any.whl
Size	3.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`2a47b1faf429a227ba4e7341c654470469184410e566d04ddbf7c58db3c13894`
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Upload date	Aug 5, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`uv/0.8.17`