AI for Robotics培训

Edge AI enables artificial intelligence models to run directly on embedded or resource-constrained devices, reducing latency and power consumption while increasing autonomy and privacy in robotic systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level embedded developers and robotics engineers who wish to implement machine learning inference and optimization techniques directly on robotic hardware using TinyML and edge AI frameworks.

By the end of this training, participants will be able to:

• Understand the fundamentals of TinyML and edge AI for robotics.
• Convert and deploy AI models for on-device inference.
• Optimize models for speed, size, and energy efficiency.
• Integrate edge AI systems into robotic control architectures.
• Evaluate performance and accuracy in real-world scenarios.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice using TinyML and edge AI toolchains.
• Practical exercises on embedded and robotic hardware platforms.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Reinforcement learning (RL) is a machine learning paradigm where agents learn to make decisions by interacting with an environment. In robotics, RL enables autonomous systems to develop adaptive control and decision-making capabilities through experience and feedback.

This instructor-led, live training (online or onsite) is aimed at advanced-level machine learning engineers, robotics researchers, and developers who wish to design, implement, and deploy reinforcement learning algorithms in robotic applications.

By the end of this training, participants will be able to:

• Understand the principles and mathematics of reinforcement learning.
• Implement RL algorithms such as Q-learning, DDPG, and PPO.
• Integrate RL with robotic simulation environments using OpenAI Gym and ROS 2.
• Train robots to perform complex tasks autonomously through trial and error.
• Optimize training performance using deep learning frameworks like PyTorch.

Format of the Course

• Interactive lecture and discussion.
• Hands-on implementation using Python, PyTorch, and OpenAI Gym.
• Practical exercises in simulated or physical robotic environments.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

OpenCV is an open-source computer vision library that enables real-time image processing, while deep learning frameworks such as TensorFlow provide the tools for intelligent perception and decision-making in robotic systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level robotics engineers, computer vision practitioners, and machine learning engineers who wish to apply computer vision and deep learning techniques for robotic perception and autonomy.

By the end of this training, participants will be able to:

• Implement computer vision pipelines using OpenCV.
• Integrate deep learning models for object detection and recognition.
• Use vision-based data for robotic control and navigation.
• Combine classical vision algorithms with deep neural networks.
• Deploy computer vision systems on embedded and robotic platforms.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice using OpenCV and TensorFlow.
• Live-lab implementation on simulated or physical robotic systems.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

ROS 2 (Robot Operating System 2) is an open-source framework designed to support the development of complex and scalable robotic applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level robotics engineers and developers who wish to implement autonomous navigation and SLAM (Simultaneous Localization and Mapping) using ROS 2.

By the end of this training, participants will be able to:

• Set up and configure ROS 2 for autonomous navigation applications.
• Implement SLAM algorithms for mapping and localization.
• Integrate sensors such as LiDAR and cameras with ROS 2.
• Simulate and test autonomous navigation in Gazebo.
• Deploy navigation stacks on physical robots.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice using ROS 2 tools and simulation environments.
• Live-lab implementation and testing on virtual or physical robots.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Robotics 是人工智能（AI）中的一个领域，涉及智能和高效机器的编程与设计。

本课程为讲师指导的培训（线上或线下），面向希望通过基本AI方法编程和创建机器人的工程师。

在本培训结束时，参与者将能够：

• 实现滤波器（卡尔曼和粒子滤波器），使机器人能够定位环境中的移动物体。
• 实现搜索算法和运动规划。
• 实现PID控制，以调节机器人在环境中的运动。
• 实现SLAM算法，使机器人能够绘制未知环境的地图。

课程形式

• 互动讲座与讨论。
• 大量练习与实践。
• 在实时实验室环境中进行实际操作。

课程定制选项

• 如需为此课程定制培训，请联系我们进行安排。

在这项由讲师指导的中国（线上或线下）培训中，学员将学习用于编程不同类型机器人的各种技术、框架和方法，这些机器人将应用于核技术和环境系统领域。

本课程为期6周，每周5天，每天4小时。课程内容包括讲座、讨论以及在实验室环境中的实际操作机器人开发。学员将完成各种与其工作相关的实际项目，以实践所学知识。

本课程的目标硬件将通过模拟软件进行3D模拟。编程机器人将使用ROS（机器人操作系统）开源框架、C++和Python。

培训结束后，学员将能够：

• 理解机器人技术中的关键概念。
• 理解并管理机器人系统中软件与硬件的交互。
• 理解并实现支撑机器人技术的软件组件。
• 构建并操作一个模拟的机械机器人，使其能够通过视觉、感知、处理、导航和语音与人互动。
• 理解构建智能机器人所需的人工智能要素（机器学习、深度学习等）。
• 实现过滤器（卡尔曼和粒子）以使机器人能够定位其环境中的移动物体。
• 实现搜索算法和运动规划。
• 实现PID控制以调节机器人在环境中的运动。
• 实现SLAM算法以使机器人能够绘制未知环境的地图。
• 通过深度学习扩展机器人执行复杂任务的能力。
• 在现实场景中测试和排除机器人故障。

在这次由讲师指导的线下或线上培训中，学员将学习编程不同类型机器人所需的各种技术、框架和技巧，这些机器人将应用于核技术和环境系统领域。

本课程为期4周，每周5天。每天4小时，包括讲座、讨论和在实验室环境中的实际操作机器人开发。学员将完成各种适用于其工作的实际项目，以实践所学的知识。

本课程的目标硬件将通过模拟软件进行3D模拟。代码随后将加载到物理硬件（如Arduino或其他）上进行最终部署测试。将使用ROS（机器人操作系统）开源框架、C++和Python来编程机器人。

培训结束后，学员将能够：

• 理解机器人技术中的关键概念。
• 理解并管理机器人系统中软件与硬件的交互。
• 理解并实现支撑机器人技术的软件组件。
• 构建并操作一个模拟的机械机器人，使其能够通过视觉、感知、处理、导航和语音与人类互动。
• 理解构建智能机器人所需的人工智能元素（如机器学习、深度学习等）。
• 实现滤波器（卡尔曼和粒子滤波器），使机器人能够定位环境中的移动物体。
• 实现搜索算法和运动规划。
• 实现PID控制，以调节机器人在环境中的运动。
• 实现SLAM算法，使机器人能够绘制未知环境的地图。
• 在真实场景中测试和调试机器人。