Steven Palma
2d8f5f314e
* perf(libero): skip the discarded scene rebuild on reset LIBERO's OffScreenRenderEnv defaults to hard_reset=True, so every reset() frees the MjSim, re-serialises the scene with model.get_xml(), recompiles it with MjSim.from_xml_string(), constructs a fresh offscreen GL context and re-wires every observable. When init states are in use, LiberoEnv.reset() immediately calls set_init_state(), which overwrites the whole sim state -- so all of that work is discarded. This passes hard_reset=not init_states instead. Without init states the randomisation reset() performs is the only thing placing the objects, so the hard reset is kept. Measured on an RTX 3060 Ti (EGL, robosuite 1.4.0, mujoco 3.2.7, 256x256 x2 cameras), through LiberoEnv.reset(), fresh env per arm: suite hard soft saved libero_spatial 1697 ms 233 ms 1464 ms libero_object 1394 ms 172 ms 1222 ms libero_goal 1177 ms 164 ms 1013 ms libero_10 1528 ms 207 ms 1321 ms Equivalence ----------- Immediately after set_init_state, qpos, qvel, ctrl and act are bit-identical between the two paths on every suite tested. After the 10 settle steps that reset() runs, 9 of 41 qpos entries differ: robot0_joint1..7 (<= 2.4e-5 rad) and gripper0_finger_joint1/2 (<= 2.1e-4 rad). No object joint differs on any suite. The drift is driven by the gripper component of the settle action ([0,0,0,0,0,0,-1]); replacing it with zeros keeps the two paths bit-identical for 12 further steps, and with num_steps_wait=0 there is no divergence at all. Wrist-camera pixels can differ by up to ~87/255, because a sub-millimetre finger displacement crosses rasterisation boundaries at 256x256. The pixel metric badly overstates the physical difference here; 2.1e-4 rad is 0.012 degrees. So this is not bit-identical end to end, and reviewers should decide whether 0.012 degrees of gripper drift is acceptable for the benchmark. It does not change object placement, which is what the fixed init states exist to control. * refactor(env): config param libero + docs --------- Co-authored-by: Dimitar Dimitrov <dvdimitrov13@gmail.com>
Generating the documentation
To generate the documentation, you first have to build it. Several packages are necessary to build the doc, you can install them with the following command, at the root of the code repository:
pip install -e . -r docs-requirements.txt
You will also need nodejs. Please refer to their installation page
NOTE
You only need to generate the documentation to inspect it locally (if you're planning changes and want to
check how they look before committing for instance). You don't have to git commit the built documentation.
Building the documentation
Once you have setup the doc-builder and additional packages, you can generate the documentation by
typing the following command:
doc-builder build lerobot docs/source/ --build_dir ~/tmp/test-build
You can adapt the --build_dir to set any temporary folder that you prefer. This command will create it and generate
the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite
Markdown editor.
Previewing the documentation
To preview the docs, first install the watchdog module with:
pip install watchdog
Then run the following command:
doc-builder preview lerobot docs/source/
The docs will be viewable at http://localhost:3000. You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
NOTE
The preview command only works with existing doc files. When you add a completely new file, you need to update _toctree.yml & restart preview command (ctrl-c to stop it & call doc-builder preview ... again).
Adding a new element to the navigation bar
Accepted files are Markdown (.md).
Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
the filename without the extension in the _toctree.yml file.
Renaming section headers and moving sections
It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
Sections that were moved:
[ <a href="#section-b">Section A</a><a id="section-a"></a> ]
and of course, if you moved it to another file, then:
Sections that were moved:
[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
Use the relative style to link to the new file so that the versioned docs continue to work.
For an example of a rich moved sections set please see the very end of the transformers Trainer doc.
Adding a new tutorial
Adding a new tutorial or section is done in two steps:
- Add a new file under
./source. This file can either be ReStructuredText (.rst) or Markdown (.md). - Link that file in
./source/_toctree.ymlon the correct toc-tree.
Make sure to put your new file under the proper section. If you have a doubt, feel free to ask in a Github Issue or PR.
Writing source documentation
Values that should be put in code should either be surrounded by backticks: `like so`. Note that argument names
and objects like True, None or any strings should usually be put in code.
Writing a multi-line code block
Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
```
# first line of code
# second line
# etc
```
Adding an image
Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted dataset like
the ones hosted on hf-internal-testing in which to place these files and reference
them by URL. We recommend putting them in the following dataset: huggingface/documentation-images.
If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
to this dataset.