Note: this guide has been written for CSCI 545, Introduction to Robotics, taught by Daniel Seita in Spring 2024. We have a policy that students must state that they have read this guide before requesting software help. This is designed for the benefit of both the course staff but also for the students.
Note: updated for Fall 2025.
This is a short guide on how to report code-related errors. If you run into
| """ | |
| Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. | |
| NVIDIA CORPORATION and its licensors retain all intellectual property | |
| and proprietary rights in and to this software, related documentation | |
| and any modifications thereto. Any use, reproduction, disclosure or | |
| distribution of this software and related documentation without an express | |
| license agreement from NVIDIA CORPORATION is strictly prohibited. | |
| Franka Cube Pick |
| """ | |
| Runs IK to get the UR5 end-effector to reach a target. Inspect CPU vs GPU mode. | |
| Use a yellow sphere to show the tip of the end-effector, and a blue sphere to | |
| show the target. These spheres are only used for debugging / visualization. | |
| """ | |
| from isaacgym import gymapi | |
| from isaacgym import gymutil | |
| from isaacgym import gymtorch | |
| from isaacgym.torch_utils import (quat_conjugate, quat_mul, quat_apply) | |
| import numpy as np |
| import cv2 | |
| import rospy | |
| import argparse | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from cv_bridge import CvBridge | |
| from sensor_msgs.msg import Image | |
| class Segmenter(): | |
| def __init__(self, args): |
| import matplotlib | |
| matplotlib.use('Agg') | |
| import matplotlib.pyplot as plt | |
| plt.style.use('seaborn') | |
| import numpy as np | |
| nrows, ncols = 1, 2 | |
| fig, ax = plt.subplots(nrows, ncols, sharey=False, squeeze=True, figsize=(9*ncols, 6*nrows)) | |
| x = np.arange(100) | |
| y0 = x + np.random.normal(loc=0.0, scale=10.0, size=100) |
| import gym | |
| import numpy as np | |
| import imageio | |
| # Make the environment and the initial observation `o`. | |
| env_name = 'Walker2d-v3' | |
| env = gym.make(env_name) | |
| obs_dim = env.observation_space.shape | |
| act_dim = env.action_space.shape[0] | |
| act_limit = env.action_space.high[0] |
| """ | |
| How we might sample from a log-uniform distribution | |
| https://stats.stackexchange.com/questions/155552/what-does-log-uniformly-distribution-mean | |
| Only run one of these three cases at a time, otherwise the plots update each | |
| other. Run with these versions: | |
| matplotlib 3.2.1 | |
| numpy 1.18.3 | |
| """ |
| """Some exception testing. | |
| The code in the `try` block will stop as soon as any exception is encountered. | |
| https://realpython.com/python-exceptions/ | |
| https://realpython.com/the-most-diabolical-python-antipattern/ | |
| """ | |
| a = 5 |
| I am testing different PyTorch transforms on my data, and saving sample | |
| images as they are loaded during training. See examples below. |
| import torch | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| from torch.optim import lr_scheduler | |
| import torchvision.models as models | |
| from torchvision import datasets, transforms | |
| import copy, cv2, os, sys, pickle, time | |
| import numpy as np | |
| from os.path import join |
