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https://github.com/ashrithsagar/multireflfd-tpgp

Learning Multi-Reference Frame Skills from Demonstration with Task-Parameterized Gaussian Processes (TPGP)

https://github.com/ashrithsagar/multireflfd-tpgp

Science Score: 39.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 2 DOI reference(s) in README
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    Academic publication links
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    Scientific vocabulary similarity
    Low similarity (11.5%) to scientific vocabulary

Keywords

gaussian-mixture-models gaussian-processes imitation-learning learning-from-demonstration probabilistic-robotics
Last synced: 9 months ago · JSON representation

Repository

Learning Multi-Reference Frame Skills from Demonstration with Task-Parameterized Gaussian Processes (TPGP)

Basic Info
  • Host: GitHub
  • Owner: AshrithSagar
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 1.98 MB
Statistics
  • Stars: 2
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
gaussian-mixture-models gaussian-processes imitation-learning learning-from-demonstration probabilistic-robotics
Created about 1 year ago · Last pushed 12 months ago
Metadata Files
Readme Changelog License

README.md

MultiRefLfD-TPGP

GitHub GitHub repo size Ruff

Learning Multi-Reference Frame Skills from Demonstration with Task-Parameterized Gaussian Processes (TPGP)

DOI | PDF

Installation

Clone the repo ```shell git clone https://github.com/AshrithSagar/MultiRefLfD-TPGP cd MultiRefLfD-TPGP ```
Install uv Install [`uv`](https://docs.astral.sh/uv/), if not already. Check [here](https://docs.astral.sh/uv/getting-started/installation/) for installation instructions. It is recommended to use `uv`, as it will automatically install the dependencies in a virtual environment. If you don't want to use `uv`, skip to the next step. TL;DR: Just run ```shell curl -LsSf https://astral.sh/uv/install.sh | sh ```

The dependencies are listed in the pyproject.toml file.

Install the package in editable mode (recommended):

```shell

Using uv

uv pip install -e .

Or with pip

pip install -e . ```

Additional config ```shell uv tool install bump-my-version ```

Usage

[!WARNING] WIP

Use the lfd module as a library.

```python import lfd

D0, _ = lfd.utils.loaddatawith_phi("s")

fdset = lfd.utils.transformdata(D0) P = lfd.alignment.computeP(fdset) D0star = lfd.alignment.align_demonstrations(fdset, P)

lfd.alignment.plotkeypoints(fdset, P) lfd.alignment.plotalignments(fdset, D0_star, P)

X = lfd.utils.transformdata(D0star) ```

Run scripts directly using uv run.

shell uv run lfd/run.py

References

Owner

  • Name: Ashrith Sagar
  • Login: AshrithSagar
  • Kind: user

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