メカトロニクス向けの人工知能 (AI)のトレーニングコース

ロボティクスのための人工知能 (AI) は、機械学習、制御システム、センサ融合を組み合わせて、環境を認識し、推論を行い、自律的に行動することができる知能型ロボットを創造します。ROS 2、TensorFlow、OpenCV などの現代的なツールを使用することで、エンジニアは今やリアルワールドの環境をナビゲーションし、計画し、対話することができるロボットを設計できるようになりました。

このインストラクターリードの実践的なトレーニング（オンラインまたはオンサイト）は、現在のオープンソーステクノロジーやフレームワークを使用して、中級レベルのエンジニアが AI 駆動型ロボットシステムを開発し、訓練し、展開することを目指しています。

このトレーニングを終了した参加者は次のようなことができるようになります：

• Python と ROS 2 を使用してロボットの動作を構築し、シミュレーションする。
• カルマンフィルタやパーティクルフィルタを用いて位置推定と追跡を行う。
• OpenCV を使用して感覚処理と物体検出を行うためにコンピュータビジョン技術を適用する。
• TensorFlow を使用して動作予測と学習ベースの制御を行う。
• SLAM（同時局所化とマッピング）を統合して自律的なナビゲーションを実現する。
• 強化学習モデルを開発して、ロボットの意思決定を改善する。

コース形式

• 双方向の講義とディスカッション。
• ROS 2 と Python を使用した手動実装。
• シミュレーションおよびリアルなロボット環境での実践的な演習。

コースカスタマイズオプション

このコースのカスタマイズ版を依頼するには、お問い合わせください。

In this instructor-led, live training in 日本 (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.

The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.

The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.

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

• Understand the key concepts used in robotic technologies.
• Understand and manage the interaction between software and hardware in a robotic system.
• Understand and implement the software components that underpin robotics.
• Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
• Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
• Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
• Implement search algorithms and motion planning.
• Implement PID controls to regulate a robot's movement within an environment.
• Implement SLAM algorithms to enable a robot to map out an unknown environment.
• Extend a robot's ability to perform complex tasks through Deep Learning.
• Test and troubleshoot a robot in realistic scenarios.

In this instructor-led, live training in 日本 (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.

The 4-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.

The target hardware for this course will be simulated in 3D through simulation software. The code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.

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

• Understand the key concepts used in robotic technologies.
• Understand and manage the interaction between software and hardware in a robotic system.
• Understand and implement the software components that underpin robotics.
• Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
• Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
• Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
• Implement search algorithms and motion planning.
• Implement PID controls to regulate a robot's movement within an environment.
• Implement SLAM algorithms to enable a robot to map out an unknown environment.
• Test and troubleshoot a robot in realistic scenarios.

ROS 2 (Robot Operating System 2)は、複雑でスケーラブルなロボットアプリケーションの開発を支援するオープンソースフレームワークです。

この講師主導のライブトレーニング（オンラインまたはオンサイト）は、中級レベルのロボティクスエンジニアやデベロッパー向けで、ROS 2を使用して自律ナビゲーションとSLAM（Simultaneous Localization and Mapping）を実装することを目指しています。

このトレーニング終了時には、参加者は以下のことができます：

• 自律ナビゲーションアプリケーションのためのROS 2を設定および構成する。
• マッピングとローカライゼーション用にSLAMアルゴリズムを実装する。
• LIDARやカメラなどのセンサーをROS 2に統合する。
• Gazeboで自律ナビゲーションをシミュレーションおよびテストする。
• 物理的なロボットにナビゲーションスタックをデプロイする。

コースの形式

• 対話型の講義とディスカッション。
• ROS 2ツールやシミュレーション環境を使用した手順実習。
• 仮想または物理的なロボットでのライブラボ実装およびテスト。

コースのカスタマイズオプション

• このコースのカスタマイズトレーニングを希望する場合は、お問い合わせください。

The Azure Bot Service combines the power of the Microsoft Bot Framework and Azure functions to enable rapid development of intelligent bots.

In this instructor-led, live training, participants will learn how to easily create an intelligent bot using Microsoft Azure

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

• Learn the fundamentals of intelligent bots
• Learn how to create intelligent bots using cloud applications
• Understand how to use the Microsoft Bot Framework, the Bot Builder SDK, and the Azure Bot Service
• Understand how to design bots using bot patterns
• Develop their first intelligent bot using Microsoft Azure

Audience

• Developers
• Hobbyists
• Engineers
• IT Professionals

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

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.

ボットまたはチャットボットは、さまざまなメッセージングプラットフォーム上でユーザーとのやり取りを自動化し、ユーザーが人間と話さなくても迅速に作業を行うためのコンピュータアシスタントです。

この講師主導のライブトレーニングでは、参加者はBot開発ツールとフレームワークを使用してサンプルチャットボットを作成する手順を学びながら、Botの開発を始めることについて学びます。

このトレーニング終了時には、参加者には以下のことができるようになります：

• ボットの異なる用途と応用について理解する
• ボットの開発プロセス全体を理解する
• ボット構築に使用されるさまざまなツールやプラットフォームを探索する
• Facebook Messenger用のサンプルチャットボットを構築する
• Microsoft Bot Frameworkを使用してサンプルチャットボットを構築する

対象者

• 自分自身のBotを作成に興味のある開発者

コース形式

• 講義とディスカッション、演習、および実践的な練習を組み合わせたもの

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.

この講師主導のライブトレーニング（オンラインまたは対面）は、中級レベルの参加者向けに設計されており、現代の職場における協働ロボット（コボット）やその他の人間中心のAIシステムの役割を探求することを目指しています。

このトレーニングの終了時には、参加者は以下のことができるようになります：

• 人間中心の物理AIの原理とその応用を理解する。
• 協働ロボットが職場の生産性向上に果たす役割を探る。
• 人間と機械の相互作用における課題を特定し、解決する。
• 人間とAI駆動システムとの協調を最適化するワークフローを設計する。
• AI統合職場における革新性と適応力の文化を促進する。

This instructor-led, live training in 日本 (online or onsite) is aimed at advanced-level robotics engineers and AI researchers who wish to utilize Multimodal AI for integrating various sensory data to create more autonomous and efficient robots that can see, hear, and touch.

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

• Implement multimodal sensing in robotic systems.
• Develop AI algorithms for sensor fusion and decision-making.
• Create robots that can perform complex tasks in dynamic environments.
• Address challenges in real-time data processing and actuation.

This instructor-led, live training in 日本 (online or onsite) is aimed at intermediate-level participants who wish to enhance their skills in designing, programming, and deploying intelligent robotic systems for automation and beyond.

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

• Understand the principles of Physical AI and its applications in robotics and automation.
• Design and program intelligent robotic systems for dynamic environments.
• Implement AI models for autonomous decision-making in robots.
• Leverage simulation tools for robotic testing and optimization.
• Address challenges such as sensor fusion, real-time processing, and energy efficiency.

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.

Safe & Explainable Robotics is a comprehensive training focused on the safety, verification, and ethical governance of robotic systems. The course bridges theory and practice by exploring safety case methodologies, hazard analysis, and explainable AI approaches that make robotic decision-making transparent and trustworthy. Participants will learn how to ensure compliance, verify behaviors, and document safety assurance in line with international standards.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to apply verification, validation, and explainability principles to ensure the safe and ethical deployment of robotic systems.

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

• Develop and document safety cases for robotic and autonomous systems.
• Apply verification and validation techniques in simulation environments.
• Understand explainable AI frameworks for robotics decision-making.
• Integrate safety and ethics principles into system design and operation.
• Communicate safety and transparency requirements to stakeholders.

Format of the Course

• Interactive lecture and discussion.
• Hands-on simulation and safety analysis exercises.
• Case studies from real-world robotics applications.

Course Customization Options

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

A Smart Robot is an Artificial Intelligence (AI) system that can learn from its environment and its experience and build on its capabilities based on that knowledge. Smart Robots can collaborate with humans, working along-side them and learning from their behavior. Furthermore, they have the capacity for not only manual labor, but cognitive tasks as well. In addition to physical robots, Smart Robots can also be purely software based, residing in a computer as a software application with no moving parts or physical interaction with the world.

In this instructor-led, live training, participants will learn the different technologies, frameworks and techniques for programming different types of mechanical Smart Robots, then apply this knowledge to complete their own Smart Robot projects.

The course is divided into 4 sections, each consisting of three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section will conclude with a practical hands-on project to allow participants to practice and demonstrate their acquired knowledge.

The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.

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

• Understand the key concepts used in robotic technologies
• Understand and manage the interaction between software and hardware in a robotic system
• Understand and implement the software components that underpin Smart Robots
• Build and operate a simulated mechanical Smart Robot that can see, sense, process, grasp, navigate, and interact with humans through voice
• Extend a Smart Robot's ability to perform complex tasks through Deep Learning
• Test and troubleshoot a Smart Robot in realistic scenarios

Audience

• Developers
• Engineers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To customize any part of this course (programming language, robot model, etc.) please contact us to arrange.

スマートロボティクスは、人工知能をロボットシステムに統合することで、より優れた知覚、意思決定、自律的な制御を実現する技術です。

この講師主導のライブトレーニング（オンラインまたはオンサイト）は、AI駆動型の知覚、計画、制御をスマート製造環境に実装することを目指す上級レベルのロボットエンジニア、システムインテグレーター、自動化リーダー向けです。

このトレーニングが終了すると、参加者は以下のことができるようになります：

• ロボットの知覚とセンサフュージョンにAI技術を理解し適用する。
• 協働ロボットや産業用ロボット向けの運動計画アルゴリズムを開発する。
• 実時間意思決定に学習ベースの制御戦略を展開する。
• 智能的なロボットシステムをスマート工場のワークフローに統合する。

コース形式

• インタラクティブな講義とディスカッション。
• 多くの演習と実践。
• ライブラボ環境でのハンズオン実装。

コースカスタマイゼーションオプション

• このコースのカスタマイズされたトレーニングを希望する場合は、ご連絡ください。