Elastica-RL-control

KO | EN

gitlite — search

#javascript #python #hacktoberfest #ai #typescript #llm #machine-learning #rust #deep-learning #linux #windows #cli #awesome-list #openai

Elastica-RL-control

(★ 32)

Code for the cases presented in the paper "Elastica: A compliant mechanics environment for soft robotic control"

Elastica-RL-control 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

## Elastica + RL benchmark cases

This repo provides supplementary code and benchmark data for the paper: [*Elastica: A compliant mechanics environment for soft robotic control*](https://ieeexplore.ieee.org/document/9369003), in IEEE Robotics and Automation Letters.

[Elastica](https://github.com/GazzolaLab/PyElastica) is a simulation environment for simulating assemblies of one-dimensional soft, slender structures using Cosserat rod theory. More information about Elastica is available on the [project website](https://cosseratrods.org). You can install the Python version of Elastica via `pip install pyelastica.`

In this repo, Elastica is interfaced with [Stable Baselines](https://github.com/hill-a/stable-baselines) to investigate how RL can dynamically control a compliant robotic arm. You can install Stable Baselines via `pip install stable-baselines[mpi]` (note: Stable Baselines only works with TesorFlow <= v1.15).
Five different RL model-free algorithms from the Stable Baselines implementations are used. Two of them are on-policy algorithms: Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) and three of them are off-policy algorithms: Soft Actor Critic (SAC), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3). Four different cases are considered with detailed explanations given in the paper.

If you discover any bugs, please open an issue and let us know. We plan to actively maintain and develop these benchmark cases.

We have provided visualization scripts we use for these cases in the `visualization` folder. Data from hyperparameter tuning is available in the `supplementary_data` folder.

### Case 1: 3D tracking of a randomly moving target
In this case, the arm is continuously tracking a randomly moving target in 3D space. Actuations are only allowed in normal and binormal directions with 6 control points in each direction.

* To replicate training using different RL algorithms, run `logging_bio_args.py` located in the `Case1/ ` folder. 
You can train policies using the five RL algorithms considered by passing the algorithm name as a command-line argument i.e. `--algo_name TRPO`.
 Also, you can control the total number of training timesteps, the random seed, and the timestep 
per batch as command-line arguments, i.e. `--total_timesteps 1E6`, `--SEED 0`, `--timesteps_per_batch 2048`.
 In addition to that, you can choose a different number of control points or torque scaling factor by changing the 
`number_of_control_points` and `alpha` variables inside `logging_bio_args.py` respectively.  
* To replicate the hyperparameter tuning, run the code `Case1/policy_training_script.py`. 
Note that the number of CPUs should be edited appropriately in the script. Runtime is 12-24 hours per individual case.
* Code for initializing the Elastica simulation environment is located in `Case1/set_environment.py`. 
Specific details on how Case 1 was implemented are in this file. 
* Post-processing scripts are located in `Case1/post_processing.py`

### Case 2: Reaching to randomly located target with a defined orientation
In this case, the arm is reaching the randomly positioned stationary target, while re-orienting itself to match the orientation
of the target. Actuations are allowed in normal, binormal, and tangent directions with 6 control points in each direction.

* To replicate training using different RL algorithms, run `logging_bio_args.py` located in the `Case2/ ` folder. 
You can train policies using the five RL algorithms considered by passing the algorithm name as a command-line argument 
i.e. `--algo_name TRPO`. Also, you can control the total number of training timesteps, the random seed, and the timestep 
per batch as command-line arguments, i.e. `--total_timesteps 1E6`, `--SEED 0`, `--timesteps_per_batch 2048`. 
In addition to that, you can choose a different number of control points or torque scaling factor by changing the 
`number_of_control_points` and `alpha` variables inside `logging_bio_args.py` respectively.  
* To replicate the hyperparameter tuning, run the code `Case2/policy_training_script.py`. 
Note that the number of CPUs should be edited appropriately in the script. Runtime is 12-24 hours per individual case. 
* Code for initializing the Elastica simulation environment is located in `Case2/set_environment.py`. 
Specific details on how Case 2 was implemented are in this file. 
* Post-processing scripts are located in `Case2/post_processing.py`

### Case 3: Underactuated maneuvering between structured obstacles
In this case, the arm is reaching a stationary target placed behind an array of eight obstacles with an opening through
which the arm must maneuver to reach the target. Target is placed in the normal plane so that only in-plane actuation is
required. Thus actuation only in the normal direction is allowed. Case 3 has two subcases. First one is training using 
2 manually placed control points at 40% and 90% of the arm and the second one is training using 2, 4, 6, and 8 
equidistant control points. Code for manually selected control
points are located in `Case3/Case3_main-text/` folder and code for equidistant control points are
located in `Case3/Case3_SI-ctrl_pts/`.

* To replicate the manually placed two control points training:
   * Run `Case3/Case3_main-text/logging_bio_args_OnPolicy.py` and run
    `Case3/Case3_main-text/logging_bio_args_OffPolicy.py` for on-policy and off-policy algorithms
     respectively. You can train policies using the five RL algorithms considered by passing the algorithm name as a 
     command-line argument i.e. `--algo_name TRPO`. Also, you can control the total number of training timesteps, 
     the random seed, and the timestep per batch as command-line arguments, i.e. `--total_timesteps 1E6`, `--SEED 0`, `--timesteps_per_batch 2048`. 
     In addition to that, you can choose a different torque scaling factor by changing the `alpha` variable inside 
    `logging_bio_args_OnPolicy.py` or  `logging_bio_args_OffPolicy.py` depending on the policy. The number of control points is fixed for this case and it is two. 
    * To replicate the hyperparameter tuning, run the code `Case3/Case3_main-text/policy_training_script_OnPolicy.py`
    or `Case3/Case3_main-text/policy_training_script_OffPolicy.py`. 
    Note that the number of CPUs should be edited appropriately in the script. Runtime is 3-4 hours per individual case.  
    * Code for initializing the Elastica simulation environment is located in `Case3/Case3_main-text/set_environment.py`. 
    Specific details on how the first subcase of Case 3 was implemented are in this file. 
    * Post-processing scripts are located in `Case3/Case3_main-text/post_processing.py`.

* To replicate the equidistantly placed control points training:
   * Run `Case3/Case3_SI-ctrl_pts/logging_bio_args.py`. You can train policies using the five RL
    algorithms considered by passing the algorithm name as a command-line argument i.e. `--algo_name TRPO`. 
    Also, you can control the total number of training timesteps, the random seed, and the timestep per batch as 
    command-line arguments, i.e. `--total_timesteps 1E6`, `--SEED 0`, `--timesteps_per_batch 2048`. In addition to that,
     you can choose a different number of control points or torque scaling factor by changing the `number_of_control_points` 
     and `alpha` variables inside `logging_bio_args_OnPolicy.py` or  `logging_bio_args_OffPolicy.py`respectively. 
    * To replicate the hyperparameter tuning, run the code `Case3/Case3_SI-ctrl_pts/policy_training_script.py`.
    Note that the number of CPUs should be edited appropriately in the script. Runtime is 3-4 hours per individual case.  
    * Code for initializing the Elastica simulation environment is located in `Case3/Case3_SI-ctrl_pts/set_environment.py`. 
    Specific details on how the second subcase of Case 3 was implemented are in this file. 
    * Post-processing scripts are located in `Case3/Case3_SI-ctrl_pts/post_processing.py`.

### Case 4: Underactuated maneuvering between unstructured obstacles
In this case, the arm is reaching a stationary target by maneuvering around an unstructured nest of twelve randomly located
obstacles. Actuation for this case is similar to Case 3, using two manually placed control points at 40% and 90% of the arm. 
Different than Case 3 actuations in normal and binormal directions are allowed.

* To replicate training using different RL algorithms, run `logging_bio_args.py` located in the `Case4/ ` folder. 
You can train policies using the five RL algorithms considered by passing the algorithm name as a command-line argument 
i.e. `--algo_name TRPO`. Also, you can control the total number of training timesteps, the random seed, and the timestep 
per batch as command-line arguments, i.e. `--total_timesteps 1E6`, `--SEED 0`, `--timesteps_per_batch 2048`. 
In addition to that, you can choose a different torque scaling factor by changing the `alpha` variable inside 
`logging_bio_args.py`. The number of control points is fixed for this case and it is two. 
* To replicate the hyperparameter tuning, run the code `Case4/policy_training_script.py`. 
Note that the number of CPUs should be edited appropriately in the script. Runtime is 6-8 hours per individual case. 
* Code for initializing the Elastica simulation environment is located in `Case4/set_environment.py`. 
Specific details on how Case 2 was implemented are in this file. 
* Post-processing scripts are located in `Case4/post_processing.py`

## Citation
We ask that any publications which use these benchmark cases cite the original paper:

Naughton, Sun, Tekinalp, Parthasarathy, Chowdhary and Gazzola, <strong>Elastica: A compliant mechanics environment for soft robotic control</strong>, IEEE Robotics and Automation Letters, 2021. doi: [10.1109/LRA.2021.3063698](https://doi.org/10.1109/LRA.2021.3063698)
```
@article{Naughton2021,
  author={Naughton, Noel and Sun, Jiarui and Tekinalp, Arman and Parthasarathy, Tejaswin and Chowdhary, Girish and Gazzola, Mattia},
  journal={IEEE Robotics and Automation Letters}, 
  title={Elastica: A compliant mechanics environment for soft robotic control}, 
  year={2021},
  volume={6},
  number={2},
  pages={3389-3396},
  doi={10.1109/LRA.2021.3063698}
}
```

Elastica-RL-control

Elastica-RL-control 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Elastica-RL-control

Elastica-RL-control 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README