Ans: Officially no, but all written materials, slides, exams will be in English.
ROS
Class Materials: Link
Paper List: Link
Overleaf Cheat Sheet Template: Link
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.
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)
研究專長: Robotic Vision, Navigation, Simultaneous Localization and Mapping (SLAM), Marine Robotics, Search and Rescure Robots
Students form teams, with 2-4 people, to work on hands-on tutorials, including a few in-class activities and (take home) assignments. Each team should use the provided mobile manipulation platform (Locobot) and develop a project to demo and present in final presentation. There is no final report.
There will be a final exam (handwriting with cheat sheets) 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.
* The format of the doctoral qualifying exam will be similar to the final exam.
| Week | Date | Topic | Lecture/Discussion | Lab |
|---|---|---|---|---|
| I | 2/22 | Intro | Introduction | Intro to Colab and Python Refresher (Colab) |
| II | 3/1 | Holidays | 和平紀念日補假 | |
| III | 3/8 | Let's get you a robot | Robotic System | ROS (Colab) |
| IV | 3/15 | 3D Representations, Joints, TF | open3d (Colab) | |
| V | 3/22 | Basic Pick and Place | Motion Planing | RRT (Colab) |
| VI | 3/29 | PyRobot (Real Robot) | ||
| VII | 4/5 | Holidays | 兒童節及民族掃墓節調整放假(5日-6日) | |
| VIII | 4/12 | 期中考週 - Project Proposal(書面報告) | Pyrobot 2 (Real Robot) | |
| IX | 4/19 | Geometric Pose Estimation | ICP (Colab) | |
| X | 4/26 | Bin Picking | Grasping | Grasping Selection (Colab) |
| XI | 5/3 | State Machine | PyRobot 3 (Real Robot) | |
| XII | 5/10 | Object Classification and Segmentation | CNN Classification | PyTorch (Colab) |
| XII | 5/17 | Transfer Learninng | Transfer Learninng (Colab) | |
| XIII | 5/24 | Segmentation | Semantic Segmentation (Colab) | |
| XIV | 5/31 | Deep Learning for Grasping | GraspNet (Colab) | |
| XV | 6/7 | Smach | Smach (Colab) | |
| XVI | 6/14 | Holidays | 端午節 | |
| XVII | 6/21 | Final Exams (Handwriting with Cheat Sheets) | ||
| XVIII | 6/28 to 7/30 | Final Project | Project topics or Mini-Competition on Gazebo Simulation |