dineshj1

@article{kunapuli2025leveling,
title={Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking},
author={Pratik Kunapuli and Jake Welde and Dinesh Jayaraman and Vijay Kumar},
abstract={Learning-based control approaches like reinforcement learning (RL) have recently produced a slew of impressive results for tasks like quadrotor trajectory tracking and drone racing. Naturally, it is common to demonstrate the advantages of these new controllers against established methods like analytical controllers. We observe, however, that reliably comparing the performance of these very different classes of controllers is more complicated than might appear at first sight. As a case study, we take up the problem of agile tracking of an end-effector for a quadrotor with a fixed-arm. We develop a set of best practices for synthesizing the best RL and Geometric controllers for benchmarking. In the process, we fix widely prevalent RL-favoring biases in prior studies that 

dineshj1

# First make sure you pip install google-api-python-client
from apiclient.discovery import build
from httplib2 import Http
from oauth2client import file, client, tools

def store_opts(opt):  # opt is the object returned by argparse
    params = dict(opt.__dict__)
    
   # here I remove parameters I don't want recorded in google sheets
    params.pop('retain_in_test', None)

dineshj1

# Written with help from http://warmspringwinds.github.io/tensorflow/tf-slim
# /2016/12/21/tfrecords-guide/
import os

import ipdb
import numpy as np
import tensorflow as tf
from tqdm import tqdm

tf_dir = './'

dineshj1

# Dinesh Jayaraman

# Based on code by
# Authors: Fabian Pedregosa <[email protected]>
#          Olivier Grisel <[email protected]>
#          Mathieu Blondel <[email protected]>
#          Gael Varoquaux
# License: BSD 3 clause (C) INRIA 2011

print(__doc__)

dineshj1

%demo_textprogressbar
%This a demo for textprogressbar script
textprogressbar('calculating outputs: ');
for i=1:100,
    textprogressbar(i);
    pause(0.1);
end
textprogressbar('done');

dineshj1

import argparse
import time

start_time=time.time();
################## Argument Parsing #####################################
parser=argparse.ArgumentParser();
parser.add_argument('-s','--solver', default='', type=str); # if empty, solver is created, else read
parser.add_argument('-res', '--resume_from', default='', type=str); #if not empty, resumes training from given file
parser.add_argument('-ft', '--finetune_from', default='', type=str);

dineshj1

function submitjobs(nosubmit, numjobs, main)
  try
    xlwrite_path='../data_utils/xlwrite/';
    addpath(xlwrite_path);
    javaaddpath([xlwrite_path 'poi_library/poi-3.8-20120326.jar']);
    javaaddpath([xlwrite_path 'poi_library/poi-ooxml-3.8-20120326.jar']);
    javaaddpath([xlwrite_path 'poi_library/poi-ooxml-schemas-3.8-20120326.jar']);
    javaaddpath([xlwrite_path 'poi_library/xmlbeans-2.3.0.jar']);
    javaaddpath([xlwrite_path 'poi_library/dom4j-1.6.1.jar']);
    javaaddpath([xlwrite_path 'poi_library/stax-api-1.0.1.jar']);

dineshj1

from tsne import bh_sne
import numpy as np
from skimage.transform import resize

from matplotlib import pyplot as plt

def gray_to_color(img):
    if len(img.shape) == 2:
        img = np.dstack((img, img, img))
    return img

dineshj1

  // generate [0..n-1]
  auto seq = [](size_t n) -> std::vector<size_t> {
      std::vector<size_t> v(n);
      for (size_t i=0; i<n; ++i) v[i] = i;
      return v;
  };
  auto index = seq(n);

  // n * n distance matrix
  std::vector<D> dists(n * n);