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<h5>ROS</h5>
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<h5>Deep Learning</h5>
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<h5>EE632, Mondays 1:20 to 4:20pm, Spring, 2021</h5>
<h5>Webpage: https://sensing-intelligent-system.github.io/</h5>
<p>Class Materials: <a href="https://drive.google.com/drive/u/1/folders/1b8ChMEnSXiJv-CbakNaY4h70s_5g0VMT" target="_blank">Link</a></p>
<p>Paper List: <a href="https://www.overleaf.com/read/xgtvpsjgzmxp" target="_blank">Link</a></p>
<p>Overleaf Cheat Sheet Template: <a href="https://www.overleaf.com/read/hvbpthdvsmgm" target="_blank">Link</a></p>
<h5>先修科目或先備能力：</h5>
<p>This course involve a fair amount of probability, linear algebra, and programming. Students who took image processing, computer vision, and creative software project are encouraged to join. Being familiar with Unix-like system, Robot Operation System (ROS), C++, and Python are desirable but not required.</p>
<li>本課程為機器人碩士學位學程必修課3選1課程</li>
<li>本課程為電控所博士班資格考課程之一</li>
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<h5>課程概述與目標：</h5>
<p>
This course covers the sensing and intelligence aspects of robots, including the fundamental and advanced topics in deep learning and object recognition, 3D perceptions and pose estimation. We focus on cutting-edge methods and implementations, and will include 50% lecture and 50% hands-on lab tutorials in mobile manipulation. We will systematically study each components from previous winning teams of the Amazon Robotics Challenge (https://www.amazonrobotics.com/#/roboticschallenge)
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<h5>授課講師: <span>Prof. </span> Nick Wang</h5>
<p>研究專長: Robotic Vision, Navigation, Simultaneous Localization and Mapping (SLAM), Marine Robotics, Search and Rescure Robots</p>
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<h5>評量方法：</h5>
<li>Colab Hands-on Tutorials and Assignments (40%)</li>
<li>Midterm Proposal and Final Project and by Locobot (25%) </li>
<li>Final Exam - Handwriting with Cheat Sheets (25%)</li>
<li>Class Participations (10%) </li>
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<h5>1. 學期作業</h5>
<p>Students form teams, with 2-4 people, to work on <strong>hands-on tutorials</strong>, including a few in-class activities and (take home) <strong>assignments</strong>. Each team should use the provided mobile manipulation platform (Locobot) and develop a project to demo and present in <strong>final presentation</strong>. There is no final report. </p>
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<h5>2. 考試狀況</h5>
<p>
There will be <strong>a final exam (handwriting with cheat sheets)</strong> for individuals. The exam will focus on the topics of deep learning and object recognition, 3D perceptions, and the recent research papers covered in the course. Students are suggested to read all papers in the given list, about 10-18 papers from top conferences or journals in related fields. Those papers are citing some fundamental papers as references. Students will be asked to write essays or draw tables/diagrams with pen and paper, and/or use individual laptop without internet communication. The questions will include (but not limited to) compare algorithms, state the limitations and challenges of existing problems, or analyze the experiment results for the papers in the given list. The exam may include a mock review process for a short article not in the given list. The evaluations of the exam will include at least two domain experts. All writing should be in English. The exam duration is 3 hours. <br>
<strong>* The format of the doctoral qualifying exam will be similar to the final exam. </strong>
</p>
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<h5>I just love the courses here</h5>
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<h6><span>Ross Cooper,</span> Student</h6>
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<tr>
<th width=8%>Week</th>
<th width=8%>Date</th>
<th width=16%>Topic</th>
<th width=38%>Lecture/Discussion</th>
<th width=30%>Lab</th>
</tr>
<tr>
<td>I</td>
<td>2/22</td>
<td>Intro</td>
<td>Introduction</td>
<td>Intro to Colab and Python Refresher (Colab)</td>
</tr>
<tr>
<td>II</td>
<td>3/1</td>
<td>Holidays</td>
<td>和平紀念日補假</td>
<td></td>
</tr>
<tr>
<td>III</td>
<td>3/8</td>
<td>Let's get you a robot</td>
<td>Robotic System</td>
<td>ROS (Colab)</td>
</tr>
<tr>
<td>IV</td>
<td>3/15</td>
<td></td>
<td>3D Representations, Joints, TF</td>
<td>open3d (Colab)</td>
</tr>
<tr>
<td>V</td>
<td>3/22</td>
<td>Basic Pick and Place</td>
<td>Motion Planing</td>
<td>RRT (Colab)</td>
</tr>
<tr>
<td>VI</td>
<td>3/29</td>
<td></td>
<td>PyRobot (Real Robot)</td>
<td></td>
</tr>
<tr>
<td>VII</td>
<td>4/5</td>
<td>Holidays</td>
<td>兒童節及民族掃墓節調整放假(5日-6日)</td>
<td></td>
</tr>
<tr>
<td>VIII</td>
<td>4/12</td>
<td></td>
<td>期中考週 - Project Proposal(書面報告)</td>
<td>Pyrobot 2 (Real Robot)</td>
</tr>
<tr>
<td>IX</td>
<td>4/19</td>
<td></td>
<td>Geometric Pose Estimation</td>
<td>ICP (Colab)</td>
</tr>
<tr>
<td>X</td>
<td>4/26</td>
<td>Bin Picking</td>
<td>Grasping</td>
<td>Grasping Selection (Colab)</td>
</tr>
<tr>
<td>XI</td>
<td>5/3</td>
<td></td>
<td>State Machine</td>
<td>PyRobot 3 (Real Robot)</td>
</tr>
<tr>
<td>XII</td>
<td>5/10</td>
<td>Object Classification and Segmentation</td>
<td>CNN Classification</td>
<td>PyTorch (Colab)</td>
</tr>
<tr>
<td>XII</td>
<td>5/17</td>
<td></td>
<td>Transfer Learninng</td>
<td>Transfer Learninng (Colab)</td>
</tr>
<tr>
<td>XIII</td>
<td>5/24</td>
<td></td>
<td>Segmentation</td>
<td>Semantic Segmentation (Colab)</td>
</tr>
<tr>
<td>XIV</td>
<td>5/31</td>
<td></td>
<td>Deep Learning for Grasping</td>
<td>GraspNet (Colab)</td>
</tr>
<tr>
<td>XV</td>
<td>6/7</td>
<td></td>
<td>Smach</td>
<td>Smach (Colab)</td>
</tr>
<tr>
<td>XVI</td>
<td>6/14</td>
<td>Holidays</td>
<td>端午節</td>
<td></td>
</tr>
<tr>
<td>XVII</td>
<td>6/21</td>
<td></td>
<td>Final Exams (Handwriting with Cheat Sheets)</td>
<td></td>
</tr>
<tr>
<td>XVIII</td>
<td>6/28 to 7/30</td>
<td>Final Project</td>
<td>Project topics or Mini-Competition on Gazebo Simulation</td>
<td></td>
</tr>
</table>
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<h5>Business for begginers</h5>
<span>By Simon Smith | March 18, 2018</span>
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<button class="collapsible-btn">Q1: Is this a English medium course?</button>
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<p>Ans: Officially no, but all written materials, slides, exams will be in English.</p>
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<button class="collapsible-btn">Q2: I don’t have much experience about building/developing robots, should I enroll in the class?</button>
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<p>Ans: Yes, you are welcome to join the class. We will cover the most-needed technical skills, and real robot platforms will be provided.</p>
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<button class="collapsible-btn">Q3: Is this course teaching-oriented or research-oriented?</button>
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<p>Ans: First of all, a quick self evaluation: how many research papers you have read/written? Do you know problem formulation? Have you designed an experiment with a hypothesis? If you don’t know much about those, you come to the right class. We wish to make this course a transition from teaching to research. We will still cover fundamental materials in the first half, with hands-on materials from MIT 2.12. In the 2nd half it will be project-based and research-oriented.</p>
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<button class="collapsible-btn">Q4: Why hands-on materials?</button>
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<p>Ans: The goal of the class is to let you build an AI-powered mobile manipulator. I believe watching a robot you build in action is the most rewarded moment, and is the best way to motivate students.</p>
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<button class="collapsible-btn">Q5: Why should we have a final exam?</button>
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<p>Ans: This course is one of the qualifying exam subjects in the PhD program of the Electrical and Control Engineering. I believe the current topics about deep learning and intelligent robots, as well as how to read paper are must-know for any graduate student.</p>
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<button class="collapsible-btn">Q6: What deep learning frameworks are used in the class and why?</button>
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<p>Ans: We will cover PyTorch. We want to run deep learning on embedded computer, and PyTorch is popular for all areas in machine learning, such as vision, NLP, and RL. More importantly we love Python. We also wish you could collect and label your own data from robots, and train your own models on GPU workstations (Colab cloud). We may cover some Tensorflow.</a></p>
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<h3>If you want to know more about <strong>ARG lab</strong>, Get in touch!</h3>
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