https://github.com/libeanim/contact-symplectic-integrator-network

Contact-Aware Symplectic Integrator Network
https://github.com/libeanim/contact-symplectic-integrator-network

Last synced: 4 months ago
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Contact-Aware Symplectic Integrator Network

• Host: GitHub
• URL: https://github.com/libeanim/contact-symplectic-integrator-network
• Owner: libeanim
• Created: 2020-08-27T16:18:09.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2023-03-22T21:30:55.000Z (over 1 year ago)
• Last Synced: 2024-01-16T10:46:16.078Z (5 months ago)
• Language: Python
• Size: 75.2 KB
• Stars: 11
• Watchers: 4
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• awesome-stars - libeanim/contact-symplectic-integrator-network - Contact-Aware Symplectic Integrator Network (Python)

README

        
# CD-Lagrange Network

This is code for the paper https://arxiv.org/abs/2102.11206

## Structure

This project has four different modules

- `environments` which contains classes to simulate the following environments:

    - Bouncing Ball

    - Pendulum

    - Newton Cradle

- `experiments`, which contains the concrete setups used in the dissertation. This includes the parameter configuration and the code for creating the plots used in this section.

- `models` contains the implementation of integrator analyzed in this work (CD-Lagrange) as well as the models it is compared to (residual network, VIN).

- `utils` contains the `TRAIN` function, which universally trains a network model given a specific environment, and the `PREDICT` function, which can be used to predict the trajectory of a given model and environment.

## Usage

Install the python requirements with

```

conda env create -f environment.yml

conda activate cdl-net

```

### Simulate experiments

To rerun the experiments from the paper, one can execute the `main.py` file, which will sequentially execute the pendulum, bouncing ball and newton's cradle experiment using the idealised touch feedback data regime.

It is possible to access the models trained in every experiment by using dot-access:

```python

from experiments import pendulum, newton_cradle

# Get the environment object of the pendulum experiment.

pendulum.env

# Get the cdl model of the pendulum experiment.

pendulum.cdl_model

# Get the environment object of the pendulum experiment.

newton_cradle.env

# Get the cdl model of the pendulum experiment.

newton_cradle.cdl_model

```

### Create new experiments

To create a new experiment, one can directly import the modules (i.e., a jupyter notebook) and set the desired parameters

```python

from environments import NewtonCradle

from models import CDLNetwork, ResNet

from utils import TRAIN, PREDICT

# Initialise the environment

env = NewtonCradle(steps=700, dt=0.01, epochs=1000)

# Generate environment data

env.generate()

# Train CD-LAGRANGE

cdl_model = TRAIN(env, CDLNetwork, name='CDL')

# Train RESNET

resnet = TRAIN(env, ResNet, name='ResNet')

# Predict trajectory given the initial state q0

cdl_data = PREDICT(env, cdl_model)

resnet_data = PREDICT(env, cdl_data)

```

In order to get a unique filename for the parameters used one can facilitate the function `env.get_filename(name, suffix='png')`.

This is useful for saving plots, for example.

## Acknowledgement

Thanks to Steindor, Alex, and Marc for their excellent support.