task-perturbation-argmin

Shashank Srikanth, Mithun Babu, Houman Masnavi, Arun Kumar Singh, Karl Kruusamäe, and K. Madhava Krishna

Final Configuration Perturbation	Mid-point perturbation

This repository contains code and data required to reproduce the results of "Fast Adaptation of Manipulator Trajectories to Task Perturbation By Differentiating through the Optimal Solution" (arXiv)

Datasets

The datasets used for our experiments are provided in the dataset folder of this repository. The 4 datasets have different level of perturbation as mentioned in the paper.

Each dataset has 6 trajectories on which we perform several perturbations for a given task. The number of perturbation trajectories in a given trajectory can be different.

Installations

The code has been tested with python3.

In order to install all the required files, create a virtualenv and install the files given in requirements.txt file.

virtualenv -p python3.7 ENV_NAME
source ENV_NAME/bin/activate
pip install -r requirements.txt

Running the code

Final Position Perturbation

In order to run the final position perturbation code, first generate the solver solutions for the given datasets using the following lines:

python generate_solver_soln.py --traj_num TRAJ_NUM  --results_path ./results/dataset_xlarge --dataset_path ../dataset/dataset_xlarge.npy

Now, pass the above results_path in the command below to get the argmin solution.

python new_formulation.py --traj_num TRAJ_NUM --results_path RESULTS_PATH --soln_path ./results/dataset_xlarge --dataset_path ../dataset/dataset_xlarge.npy

Here soln_path refers to the path where the initial solver trajectories are stored.

Final Configuration Perturbation and Mid Point Perturbation

In order to run the code, use the following:

python launch.py 2

2 here indicates the perturbration level. Look at the table below to select a valid perturbation . Now to generate historgram plots shown in paper, use:

python plot_metrics.py 2

Dataset	location	command line input	perturbation
small	dataset/dataset_small.npy	1	0.0-0.1m
medium	dataset/dataset_medium.npy	2	0.1-0.2m
large	dataset/dataset_large.npy	3	0.2-0.3m
xlarge	dataset/dataset_xlarge.npy	4	0.3-0.4m

To generate median, 25 percentile and 75 percentile plots shown in paper use:

python plot_metrics.py -1