Skip to main content

LocoFormer

Project description

LocoFormer (wip)

LocoFormer - Generalist Locomotion via Long-Context Adaptation

The gist is they trained a simple Transformer-XL in simulation on robots with many different bodies (cross-embodiment) and extreme domain randomization. When transferring to the real-world, they noticed the robot now gains the ability to adapt to insults. The XL memories span across multiple trials, which allowed the robot to learn in-context adaptation.

Sponsors

This open sourced work is sponsored by Safe Sentinel

Citations

@article{liu2025locoformer,
    title   = {LocoFormer: Generalist Locomotion via Long-Context Adaptation},
    author  = {Liu, Min and Pathak, Deepak and Agarwal, Ananye},
    journal = {Conference on Robot Learning ({CoRL})},
    year    = {2025}
}

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

locoformer-0.0.25.tar.gz (36.9 MB view details)

Uploaded Source

Built Distribution

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

locoformer-0.0.25-py3-none-any.whl (10.9 kB view details)

Uploaded Python 3

File details

Details for the file locoformer-0.0.25.tar.gz.

File metadata

  • Download URL: locoformer-0.0.25.tar.gz
  • Upload date:
  • Size: 36.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for locoformer-0.0.25.tar.gz
Algorithm Hash digest
SHA256 3391cc66b503c8578216ebfc7f5a648207a66542ecd88bb4e7ce8d17b0afda7e
MD5 6d81b066fe4430ac0be6878df15b51a7
BLAKE2b-256 96686725ea6d361da1527d56fcc9f31e0cbd17ad9288f0d49a416f4e776bc6ac

See more details on using hashes here.

File details

Details for the file locoformer-0.0.25-py3-none-any.whl.

File metadata

  • Download URL: locoformer-0.0.25-py3-none-any.whl
  • Upload date:
  • Size: 10.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for locoformer-0.0.25-py3-none-any.whl
Algorithm Hash digest
SHA256 97cc7f17fd5ae0dfe0ea37d29bb5bd71054140ae79ff2ebf5e95745b2ae13c3b
MD5 23917386c96bca0a13937429da39bd9a
BLAKE2b-256 7aac8b86ed23ee3b0131db1eeb1e26af609ce12e93ba4191f4c64d28336c579b

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