机器人学科是非常有趣的，对理论和实践的要求都比较高。掌握C/C++/Python/Matlab，能够使用V-Rep/Webots/Gazebo等仿真软件。这里需要重点强调一下仿真软件，由于学校和学生教育资金投入，仿真可以算是极低成本门槛而又有直观效果的工具了。
这里的免费是指参考书都无需购买，连下载币都不需要~
当然，机器人推荐Cozmo和Tello，成熟稳定，价格实惠，远低于1k，输入设备推荐游戏手柄和LeapMotion，输出设备伺服电机等。
知名学府的公开课（ETH，MIT，Stanford，Carnegie Mellon等），例如：
讲解了机器人学入门，运动规划，传感器，概率机器人学，蒙特卡罗定位，场景识别，同步定位和地图构建等，课程最大的特点是侧重实践，资料十分丰富具体。（链接内附有课程全部文档资料）
选择一些仿真和真实机器人，多使用，多看源码，多思考，多练习。
案例包括了地上跑，工厂用，天上飞，水里游等多场景仿真和实物。
这款机器人案例十分丰富，参考文档如下：
几乎涵盖的服务机器人的全部要点，基础概念，自动驾驶，泊车，跟随，导航，SLAM，全景图，遥控，机器学习，ROS2等。
支持版本：indigo，kinetic，melodic。
空中机器人可以参考hector_quadrotor：
有室内室外两种典型仿真环境，室外仿真如下图所示：
这是我个人最喜欢的一个案例，涵盖安装，控制，自动导航，MoveIt!，OpenCV，点云，多机器人协作等。
如今获取机器人知识的途径非常多了，并且大部分都是免费开源的，选择一些有趣的案例进行实践，才能真正掌握。
更多案例参考：
Fin
参考网址：
NumberTitleLecturers
151-0851-00LRobot DynamicsR. Y. Siegwart, M. Hutter, K. Rudin, T. Stastny
151-0854-00LAutonomous Mobile RobotsR. Y. Siegwart
151-0664-00LArtificial Intelligence for RoboticsI. Gilitschenski, C. Cadena, R. Y. Siegwart
151-0634-00LPerception and Learning for RoboticsC. Cadena, J.J. Chung, R. Y. Siegwart
Courses
Date of Lecture
Week title
Add-on slides
Lecturer
19.02.2019
Intro and Motivation
Introduction and Overview (PDF, 14.3 MB)
R. Siegwart
26.02.2019
Locomotion Concepts
Locomotion Concepts (PDF, 6.6 MB)
M. Hutter
Ex1
26.02.2019
Introduction to V-Rep simulator
Introduction to V-Rep simulator (ZIP, 3.3 MB)
I. Sa, K. Bodie
05.03.2019
Mobile Robot Kinematics
Mobile Robot Kinematics (PDF, 2.3 MB)
R. Siegwart
12.03.2019
Perception I (to 4.3)
Perception I (PDF, 1.4 MB)
R. Siegwart
19.03.2019
Perception II (to 4.4)
Perception II (PDF, 26.9 MB)
M. Chli
Ex2
19.03.2019
Kinematics & control of a differential drive vehicle
Kinematics & control of a differential drive vehicle (ZIP, 2 MB) Slides (PDF, 395 KB) Solutions (ZIP, 2 MB)
A. Vempati, M. Brunner
26.03.2019
Perception III: Image Saliency (to 4.5)
Perception III (PDF, 22.7 MB)
M. Chli
02.04.2019
Perception IV: Place Recognition & Line Fitting (to 4.5)
Perception IV (PDF, 21.7 MB)
M. Chli
Ex3
02.04.2019
Line Extraction
Line Extraction (ZIP, 1.9 MB) Slides (PDF, 706 KB) Solutions (ZIP, 1.9 MB)
H. Blum, L. Bernreiter
Q1
02.04.2019
Quiz 1
M. Grinvald, M. Breyer
09.04.2019
Localization I (to 5.2)
Localization (PDF, 1.2 MB)
R. Siegwart
16.04.2019
Localization II
Localization II (PDF, 2.4 MB)
R. Siegwart
Ex4
16.04.2019
Line-based Extended Kalman Filter
Line-based Extended Kalman Filter (ZIP, 2.1 MB) Slides (PDF, 3.2 MB) Solutions (ZIP, 2.1 MB)
H. Blum, L. Bernreiter
Week off - Easter Holiday
10
30.04.2019
SLAM I
SLAM I (PDF, 22.1 MB)
M. Chli
11
07.05.2019
SLAM II
M. Chli
Ex5
07.05.2019
EKF SLAM
EKF SLAM (ZIP, 2.1 MB)
T. Schneider, F. Tschopp
12
14.05.2019
Planning I (to 6.2)
N. Lawrance
13
21.05.2019
Planning II (to 6.3)
N. Lawrance
Ex6
21.05.2019
Dijkstra's alg. and the dynamic window
D. Dugas, R. Bähnemann
Q2
21.05.2019
Quiz 2
M. Grinvald, M. Breyer
14
28.05.2019
Summary
R. Siegwart
This course provides tools from statistics and machine learning enabling the participants to deploy them as part of typical perception pipelines. All methods provided within the course will be discussed in context of and motivated by example applications from robotics. The accompanying exercises will involve implementations and evaluations using typical robotic datasets.
The students are expected to be familiar with the following material:
The number of participants is limited to 50. Enrolment was only valid through registration until Sunday, December 18, 2016. Notifications of acceptance were sent out no on Sunday, January 15, 2017.
This course covers tools from statistics and machine learning enabling the participants to deploy these algorithms as building blocks for perception pipelines on robotic tasks. All mathematical methods provided within the course will be discussed in context of and motivated by example applications mostly from robotics. The main focus of this course are student projects on robotics, with an emphasis on robot perception.
The students are expected to be familiar with material of the "Recursive Estimation" and the "Learning and Intelligent Systems" lectures. Particularly understanding of basic machine learning concepts, stochastic gradient descent for neural networks, reinforcement learning basics, and knowledge of Bayesian Filtering are required. Furthermore, good knowledge of programming in C++ and Python is required.