This course dives deep into the integration of artificial intelligence and machine learning within robotics. You will learn to build intelligent robots capable of performing real-world tasks using ROS 2, Python, OpenCV, and advanced AI/ML techniques. By focusing on neural networks, computer vision, and natural language processing, this course will help you enhance robot functionality for complex tasks.
Artificial Intelligence for Robotics
您将学到什么
Apply AI and ML techniques to enhance robot perception and decision-making
Implement object recognition and navigation strategies using neural networks and algorithms
Integrate natural language processing to enable voice and personality features in robots
您将获得的技能
您将学习的工具
要了解的详细信息
可分享的证书
添加到您的领英档案
最近已更新！
March 2026
作业
11 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
该课程共有11个模块
In this section, we explore integrating AI into robotics, focusing on decision-making, learning, and autonomy. Key concepts include neural networks, reinforcement learning, and autonomous behavior design.
涵盖的内容
2个视频9篇阅读材料1个作业
In this section, we explore robot anatomy, subsumption architecture, and ROS 2 setup to enable practical robotic system development through structured hardware and software configuration.
涵盖的内容
1个视频5篇阅读材料1个作业
In this section, we explore systems engineering principles for robot design, focusing on use cases, storyboards, and hardware/software requirements to guide practical robotic task development.
涵盖的内容
1个视频5篇阅读材料1个作业
In this section, we explore using convolutional neural networks (CNNs) and YOLOv8 for object recognition, focusing on image processing, supervised learning, and real-world applications in robotics and AI.
涵盖的内容
1个视频5篇阅读材料1个作业
In this section, we explore training robots using reinforcement learning and genetic algorithms. Key concepts include Q-learning for grasping and GA-based path planning for autonomous manipulation.
涵盖的内容
1个视频8篇阅读材料1个作业
In this section, we explore robot speech recognition using NLP, STT, and TTS, and implement command processing with Mycroft to enhance natural language understanding and response generation.
涵盖的内容
1个视频7篇阅读材料1个作业
In this section, we explore robot navigation strategies without SLAM, focusing on AI-driven obstacle avoidance and sensor-based movement for efficient task execution.
涵盖的内容
1个视频7篇阅读材料1个作业
In this section, we explore AI decision-making tools like decision trees, path planning, and expert systems for robotics.
涵盖的内容
1个视频8篇阅读材料1个作业
In this section, we explore simulating artificial personality in robots using finite state machines and AI. Key concepts include behavior modeling and emotion simulation for practical robotic applications.
涵盖的内容
1个视频10篇阅读材料1个作业
In this section, we examine when to stop in AI development, explore robotics career paths, and assess AI risks to support informed decision-making in real-world applications.
涵盖的内容
1个视频6篇阅读材料1个作业
In this section, we will explore the foundational elements of robot communication and system design.
涵盖的内容
3篇阅读材料1个作业
位教师
提供方
从 Software Development 浏览更多内容
状态：免费试用University of Michigan
状态：预览
状态：免费试用L&T EduTech
人们为什么选择 Coursera 来帮助自己实现职业发展
Felipe M.
自 2018开始学习的学生
''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'
Jennifer J.
自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
Larry W.
自 2021开始学习的学生
''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'
Chaitanya A.
''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'
常见问题
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
更多问题
提供助学金，