All certifications
AI CERTs®
AT-420
AI+ Robotics Practitioner™
Formerly known as AI+ Robotics™ Build the Future with Smart Automation
₹21,999
Delivered by UpskillNexus, an AI CERTs® Authorized Training Partner.
About this certification
AI-Driven Robotics: Apply AI in Deep Learning, Reinforcement Learning, and smart automation Real-World Systems: Work with autonomous systems and intelligent agents Ethics & Innovation: Learn industry-aligned practices and innovation strategies Hands-On Projects: Gain experience designing, optimising, and deploying robotics solutions
What's included
Package
Instructor-led OR Self-paced course + Official exam + Digital badge
Duration
- Instructor-Led: 5 days (live or virtual)
- Self-Paced: 40 hours of content
Exam format
50 questions, 70% passing, 90 minutes, online proctored exam
Prerequisites
Basic knowledge of computer science and statistics, data analysis, fundamental AI/ML concepts, Python and R.
Course modules
Module 1: Introduction to Robotics and Artificial Intelligence (AI)
- 1.1 Overview of Robotics: Introduction, History, Evolution, and Impact
- 1.2 Introduction to Artificial Intelligence (AI) in Robotics
- 1.3 Fundamentals of Machine Learning (ML) and Deep Learning
- 1.4 Role of Neural Networks in Robotics
Module 2: Understanding AI and Robotics Mechanics
- 2.1 Components of AI Systems and Robotics
- 2.2 Deep Dive into Sensors, Actuators, and Control Systems
- 2.3 Exploring Machine Learning Algorithms in Robotics
Module 3: Autonomous Systems and Intelligent Agents
- 3.1 Introduction to Autonomous Systems
- 3.2 Building Blocks of Intelligent Agents
- 3.3 Case Studies: Autonomous Vehicles and Industrial Robots
- 3.4 Key Platforms for Development: ROS (Robot Operating System)
Module 4: AI and Robotics Development Frameworks
- 4.1 Python for Robotics and Machine Learning
- 4.2 TensorFlow and PyTorch for AI in Robotics
- 4.3 Introduction to Other Essential Frameworks
Module 5: Deep Learning Algorithms in Robotics
- 5.1 Understanding Deep Learning: Neural Networks, CNNs
- 5.2 Robotic Vision Systems: Object Detection, Recognition
- 5.3 Hands-on Session: Training a CNN for Object Recognition
- 5.4 Use-case: Precision Manufacturing with Robotic Vision
Module 6: Reinforcement Learning in Robotics
- 6.1 Basics of Reinforcement Learning (RL)
- 6.2 Implementing RL Algorithms for Robotics
- 6.3 Hands-on Session: Developing RL Models for Robots
- 6.4 Use-case: Optimizing Warehouse Operations with RL
Module 7: Generative AI for Robotic Creativity
- 7.1 Exploring Generative AI: GANs and Applications
- 7.2 Creative Robots: Design, Creation, and Innovation
- 7.3 Hands-on Session: Generating Novel Designs for Robotics
- 7.4 Use-case: Custom Manufacturing with AI
Module 8: Natural Language Processing (NLP) for Human-Robot Interaction
- 8.1 Introduction to NLP for Robotics
- 8.2 Voice-Activated Control Systems
- 8.3 Hands-on Session: Creating a Voice-command Robot Interface
- 8.4 Case-Study: Assistive Robots in Healthcare
Module 9: Practical Activities and Use-Cases
- 9.1 Hands-on Session-1: Building AI Models for Object Recognition using Python Programming
- 9.2 Hands-on Session-2: Path Planning, Obstacle Avoidance, and Localization Implementation using Python Programming
- 9.3 Hands-on Session-3: PID Controller Implementation using Python programming
- 9.4 Use-cases: Precision Agriculture, Automated Assembly Lines
Module 10: Emerging Technologies and Innovation in Robotics
- 10.1 Integration of Blockchain and Robotics
- 10.2 Quantum Computing and Its Potential
Module 11: Exploring AI with Robotic Process Automation
- 11.1 Understanding Robotic Process Automation and its use cases
- 11.2 Popular RPA Tools and Their Features
- 11.3 Integrating AI with RPA
Module 12: AI Ethics, Safety, and Policy
- 12.1 Ethical Considerations in AI and Robotics
- 12.2 Safety Standards for AI-Driven Robotics
- 12.3 Discussion: Navigating AI Policies and Regulations
Module 13: Innovations and Future Trends in AI and Robotics
- 13.1 Latest Innovations in Robotics and AI
- 13.2 Future of Work and Society: Impact of AI and Robotics
Optional Module: AI Agents for Robotics
- 1. What Are AI Agents
- 2. Key Capabilities of AI Agents in Robotics
- 3. Applications and Trends for AI Agents in Robotics
- 4. How Does an AI Agent Work
- 5. Core Characteristics of AI Agents
- 6. The Future of AI Agents in Robotics
- 7. Types of AI Agents
Why this certification matters
Demand for Certified AI & Robotics Professionals:
Organizations are seeking certified professionals who can integrate AI into robotics to optimize processes, enhance automation, and improve operational efficiency.
Risks of Mismanaging AI & Robotics:
Mismanagement of robotic systems and AI technologies can lead to operational inefficiencies and safety risks.
Role of Certification in Robotics Strategy:
Certified professionals are key in developing robotics strategies that maximize performance, safety, and compliance with industry regulations.
Career Advantage & Leadership Opportunities:
As robotics and AI continue to reshape industries, this certification offers professionals a distinct advantage, positioning them for leadership roles.
Who should enrol
Robotics Engineers Enhance robotic system design and functionality using AI for automation and control.
Mechanical Engineers: Integrate AI to optimize robotics systems and improve performance in manufacturing and production.
AI Specialists: Apply AI techniques to enhance the intelligence and autonomy of robotic systems.
IT Specialists & System Integrators: Implement AI-powered solutions to improve robotics infrastructure and communication systems.
Students & New Graduates: Build essential skills in AI and robotics to succeed in an emerging field with endless growth potential.
Tools you'll work with
OpenAI Gym
GreyOrange
Neurala
DialogflowAI Data & Robotics
AI Technical
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