Professor let me read the topics of this book. Chapter 2 - Foundations of Robot Motion DF of a Rigid Body DF of a Robot Chapter 3 - Introduction to Rigid-Body Motion Rotation Matrices! Homogeneous Transformation Matrices Chapter 4 - Forward Kinematics Chapter 5 - Velocity Kinematics and Statics Chapter 6 - Inverse Kinematics Chapter 7 - Kinematics of Closed Chains Chapter 8 - Dynamics of Open Chains Chapter 9 - Chapter Chapter Chapter Chapter

Robotic Manipulation Perception, Planning and Control https://manipulation.csail.mit.edu/Fall2021/ Lec 01 - Anatomy of a Manipulation System (2021/9/9) Lec 02 - Let’s get you a robot Lec 03 - Basic Pick and Place Lec 04 - Differential Kinematics and Trajectories Lec 05 - Differential Kinematics via Optimization Lec 06 - Geometric Perception: Cameras, Point Clouds, and Registration!!!!!! Lec 07 - Geometric Perception: Partial Views and Outliers Lec 08 - Geometric perception: Non-penetration and free-space constraints Lec 09 - Manipulation in Cluster Lec 10 Lec 12 - Deep Perception: Perceptual Representations for Lec 13

ROS2 is expected to become mature and fully replace ROS1 by 2023. ROS2 Foxy Version released in June 2020 is mature and contain several advanced features (EOL: May 2023) ROS2 Galactic Version relased in May 2021, but EOL date is Nov 2022 ROS2 provides a standard for Robotic Applications any robots can be powered by ROS2 ROS2 provides a way of separating code into reusable blocks

1. 0/1 KnapSack 494. Target Sum 1 2 3 4 5 6 7 8 Input: nums = [1,1,1,1,1], target = 3 Output: 5 Explanation: There are 5 ways to assign symbols to make the sum of nums be target 3. -1 + 1 + 1 + 1 + 1 = 3 +1 - 1 + 1 + 1 + 1 = 3 +1 + 1 - 1 + 1 + 1 = 3 +1 + 1 + 1 - 1 + 1 = 3 +1 + 1 + 1 + 1 - 1 = 3 Solution 1 - Backtrack 1 2 3 4 5 6 7 8 9 10 11 12 13 int count = 0; public int findTargetSumWays(int[] nums, int target) { backtrack(nums, target, 0, 0); return count; } public void backtrack(int[] nums, int target, int idx, int sum) { if (idx == nums.

1. Binary Classification Example Recall: Logistic Regression is an Algorithm for Binary Classification An image is store in the computer in three separate matrices corresponding to the Red, Green, and Blue color channels of the image. The three matrices have the same size as the image, for example, the resolution of the cat image is (64 pixels X 64 pixels), the three matrices (RGB) are 64 X 64 each. 1 2 3 4 5 6 7 8 9 10 11 12 13 import numpy as np def image2vector(image): """ Argument: image -- a numpy array of shape (length, height, depth) Returns: vector -- a vector of shape (length*height*depth, 1) """ vector = image.

1. Introduction and Basic Algorithm Classical Algorithms vs Deep Learning - Object Classification Object Registration Object Detection Classical Keypoint Detection Keypoint Description SVM Nearest Neighbor Search Iterative Closest Point(ICP) Background Removal Clustering Classification Deep Learning Data Collection Data Labeling Train a Network Data Collection Data Labeling Train a Network Data Collection Data Labeling Train a Network Note Lists: