Executive Post Graduate Certificate

What Is the Executive Post Graduate Certificate in Applied Robotics and AI/ML?

Key Highlights: Executive Post Graduate Certificate in Applied Robotics and AI/ML

Why Choose the IIT Kharagpur Executive Post Graduate Certification in AI And Machine Learning With Robotics?

Robotics is more than just building a machine or teaching a neural network. One also should gain knowledge about the robot's behavior, develop plans for avoiding obstacles, and make the robot's decisions by ML models.

This will serve as a great intermediary in achieving this through offering you the:

• Combined study of robotics and AI/ML: This course will combine kinematics, dynamics, control, optimization, fuzzy intelligence, deep learning, and autonomous systems.

• Hands-on approach: Practical use of a capstone (robotic system itself) rather than the theoretical knowledge about the robotics and AI/ML only.

• Structured learning path: Master robots from a basic level through control, motion planning and further AI/ML till you will be ready for industry level deployment.

• Project based work: Get involved with ROS 2, simulation tools, Python, PyTorch and open-ended robotic project development.

• Four specialisation areas: Select one area of specialization for your project, namely Industrial Automation, Drones/UAVs, Healthcare and Service Robotics, or Defence and Automation in General.

• Industry applications: Study how various industries such as automotive manufacturing, logistics, health care, agriculture, defense, and electronics use robotics and AI/ML.

What Will You Learn In This Applied Robotics and AI/ML Course?

This executive program in robotics and AI/Ml runs for approximately 6 months, along with  15 hours of readiness bridge section which is self paced and prepares you for the core modules.

The curriculum of this program is:

Bridge: Pre-Programme Readiness

Duration: 15 hours | Self-paced

Topics Covered:

• Hardware fundamentals for learners with stronger coding backgrounds

• Coding fundamentals for learners with stronger hardware backgrounds

• Readiness assessment to identify learning gaps

• Digital controller implementation

Skills Acquired:

• Revising the fundamentals needed for robotics and AI/ML

• Identifying gaps in hardware or programming knowledge

• Preparing for the main curriculum

Module 1: Robot Kinematics and Workspace Design

Weeks 1–2 | 6 live hours

Topics Covered:

• Robot anatomy, links, joints, end effectors, and degrees of freedom

• Coordinate frames, transformations, rotation matrices, and quaternions

• Forward and inverse kinematics

• Jacobians, singularities, and manipulability

• Workspace analysis, grippers, and parallel manipulators

• ROS 2 URDF modelling, TF2 transform trees, and RViz verification

Skills Acquired:

• Modelling serial and parallel manipulators

• Solving forward and inverse kinematics in code

• Analysing robot workspaces and movement constraints

• Assessing robot reach, payload, and dexterity requirements

Module 2: Robot Dynamics and Actuation

Weeks 3–4 | 6 live hours

Topics Covered:

• Newton-Euler and Lagrangian formulations

• Forward and inverse dynamics

• Inertia, friction, backlash, and compliance

• Motor selection, gearboxes, torque, and payload budgets

• Duty cycles, thermal limits, and series elastic actuation

• Contact modelling, Zero Moment Point, Gazebo, and Isaac Sim

Skills Acquired:

• Deriving and simulating multi-body robot dynamics

• Selecting actuators and transmissions for a defined task

• Assessing torque, payload, and thermal requirements

• Validating robot models through physics simulation

Module 3: Robot Control Systems and Human-Robot Collaboration

Weeks 5–7 | 9 live hours

Topics Covered:

• Encoders, IMUs, RGB-D cameras, LiDAR, and force sensors

• Sensor calibration, sensor fusion, and latency budgets

• Open-loop and closed-loop control

• PID tuning, computed torque control, and model predictive control

• Position, force, hybrid, impedance, and compliance control

• Human-robot collaboration, teleoperation, and AR/VR interfaces

• Safety assessment, emergency stops, watchdogs, and ROS 2 control

Skills Acquired:

• Designing and tuning robot controllers

• Selecting sensors for defined tasks

• Evaluating controller stability and response

• Planning safe human-robot interactions

• Working with robot control interfaces and hardware systems

Module 4: Motion Planning and Multi-Robot Navigation

Weeks 8–9 | 6 live hours

Topics Covered:

• Configuration spaces and obstacle representation

• A*, D*, potential fields, RRT, RRT*, and PRM

• Joint-space and Cartesian-space trajectories

• Velocity and torque limits

• Genetic algorithms and particle swarm optimisation for planning

• Multi-robot task allocation, collision avoidance, and swarm robotics

• ROS 2 MoveIt and Nav2

Skills Acquired:

• Developing motion planners for manipulators and mobile robots

• Generating executable robot trajectories

• Comparing motion-planning methods

• Planning collision-free routes

• Selecting planning approaches for multi-robot tasks

Module 5: Optimization and Nature-Inspired Algorithms

Weeks 10–12 | 9 live hours

Topics Covered:

• Optimization principles, problem formulation, and constraints

• Gradient-based optimisation

• Genetic algorithms and differential evolution

• Particle swarm optimisation, simulated annealing, and ant colony optimisation

• The Bonobo Optimizer

• Weighted-sum methods, NSGA-II, and Pareto fronts

• Algorithm benchmarking and parameter tuning

• Scheduling, supply-chain, resource-allocation, and manufacturing applications

Skills Acquired:

• Formulating constrained optimisation problems

• Selecting and tuning optimisation algorithms

• Comparing algorithm performance

• Interpreting Pareto fronts and trade-offs

• Applying optimisation beyond robotics

Module 6: Fuzzy Intelligence for Control and Decision-Making

Weeks 13–14 | 6 live hours

Topics Covered:

• Crisp and fuzzy sets, membership functions, and fuzzy relations

• Mamdani and Sugeno inference

• Defuzzification and rule-base construction

• Fuzzy controller design and comparison with PID

• Fuzzy clustering and fuzzy C-means

• Neuro-fuzzy systems, ANFIS, and type-2 fuzzy sets

Skills Acquired:

• Designing and tuning fuzzy controllers

• Building rule-based decision systems

• Comparing fuzzy control with PID and learned models

• Using expert knowledge when data is sparse

• Applying fuzzy intelligence to robotics and other processes

Module 7: Neural Networks, Deep Learning and Edge AI

Weeks 15–18 | 12 live hours

Topics Covered:

• Perceptrons, feed-forward networks, and backpropagation

• Radial basis function networks and self-organising maps

• Loss functions, regularisation, and hyperparameter tuning

• CNNs, attention, transformers, vision transformers, and transfer learning

• Image classification, object detection, segmentation, and pose estimation

• Dataset preparation, augmentation, and evaluation metrics

• Model compression, quantisation, and edge AI deployment

• PyTorch implementation and inference latency

Skills Acquired:

• Training and evaluating deep learning models

• Selecting network architectures for defined tasks

• Debugging models and assessing overfitting

• Building perception models for robotics

• Compressing and deploying models to edge hardware

Module 8: AI/ML in Robotics: Intelligent and Autonomous Systems

Weeks 19–22 | 12 live hours

Topics Covered:

• Robot perception and multimodal sensor fusion

• Visual and LiDAR odometry, SLAM, and localisation

• Grasp detection and manipulation policies

• Imitation learning, behaviour cloning, and diffusion policies

• Reinforcement learning, reward design, and safety constraints

• Digital twins, domain randomisation, and sim-to-real transfer

• Vision-language-action models and language-conditioned tasks

• ROS 2 integration, edge computing, and latency budgets

Skills Acquired:

• Integrating AI/ML components into a robot stack

• Working with perception and localisation systems

• Evaluating learned robot policies

• Moving from simulation to hardware testing

• Assessing autonomy, failure modes, and compute requirements

Module 9: Robotics and AI/ML in Industry: From Lab to Deployment in India

Week 23 | 3 live hours

Topics Covered:

• Automotive and electronics manufacturing

• Robotic welding, visual inspection, and PCB assembly

• Warehousing, logistics, autonomous mobile robots, and digital twins

• Defence, surveillance, and UAV/UGV applications

• Healthcare, rehabilitation, and hospital delivery

• Precision spraying and autonomous harvesting

• ROI, total cost of ownership, workforce reskilling, and safety compliance

• Regulatory and infrastructure considerations in India

Skills Acquired:

• Identifying suitable industrial robotics applications

• Assessing automation requirements and deployment constraints

• Comparing build-versus-buy options

• Evaluating ROI and total cost of ownership

• Considering safety, infrastructure, and workforce requirements

Capstone: Build and Defend a Complete Robotic System

After completing all 9 modules, you will build a capstone project in week 25, which covers all the concepts covered in the curriculum.

At week 5, you have to select one elective specialization track among 4 of the tracks. The track you select will shape the robot and application.

The Capstone in Executive Post Graduate Certificate in Applied Robotics and AI/ML Focus on:

• Select a robot application and define its requirements

• Develop the kinematic model and size actuators

• Build the controller and motion planner

• Add a learned component and test it through simulation

• Validate the robotic system on hardware

• Evaluate results and document design decisions

• Present and defend the completed project

Elective Specialisation Tracks:

1. Industrial Automation and Cobots: Develop a cobot-based system for assembling, inspecting or manipulating tasks.

2. Drones and UAV Systems: Design an unmanned aerial autonomous vehicle system incorporating a safety analysis.

3. Healthcare and Service Robotics: Develop a service or assistive robotic system for a clinical or home care task.

4. Defence and Applied Automation: Research a robotic application for surveillance, logistics, or military use.

Skills Acquired:

• Applying robotics and AI/ML concepts to a complete system

• Connecting modelling, control, planning, and learning

• Testing simulation results against hardware behaviour

• Evaluating system performance and limitations

• Presenting technical decisions during a design review

Tools and Frameworks Covered in the Executive Post Graduate Certificate in Applied Robotics and AI/ML

Among the other IIT Kharagpur online courses, this executive program in robotics with AI/ML covers a wide range of tools of both fields. 

The tools and frameworks covered in this program includes:

1. Robot Modelling and Visualisation

• URDF: Describe robot models and their components.

• TF2: Manage coordinate frames and transformations.

• RViz: Visualise and verify robot models.

2. Robot Simulation and Physics

• Gazebo: Simulate robotic systems and their physical behaviour.

• Isaac Sim: Support robot physics simulation and model validation.

3. Robot Control and Navigation

• ROS 2: Connect robot components and software.

• ros2_control: Work with robot control systems and hardware interfaces.

• MoveIt: Support robotic manipulation and motion planning.

• Nav2: Support mobile robot navigation.

4. AI/ML and Deep Learning

• Python: Develop algorithms and robotics applications.

• PyTorch: Implement and train deep learning models.

• CNNs and vision transformers: Study image-based perception.

• Reinforcement learning: Develop learned policies for robotic tasks.

• Genetic algorithms and particle swarm optimisation: Explore optimisation and planning approaches.

Who Should Enrol in the IIT Kharagpur Executive Post Graduate Certificate in Applied Robotics and AI/ML?

This IIT Kharagpur AI course along with applied robotics is relevant for you if you are:

Mechanical and Robotics Engineers

If you are someone currently working in designing, building or maintaining a physical system, this program will help you to add these AI/ML and robotics skills to your current work. You will learn topics such as optimization, fuzzy intelligence, deep learning and development of robots. 

Software and Data Engineers

Someone working with Python or machine learning already, and want to move their career into physical systems, this program will introduce you to topics such as kinematics, dynamics, planning and hardware development.

Electronics and Embedded Engineers

This program will assist electronics and embedded system engineers in designing more autonomous systems because it includes AI/ML and planning techniques.

Supply Chain, Manufacturing, and Operations Professionals

Someone working in the field of manufacturing, supply chain or operations management, they can study optimization and fuzzy intelligence methods which helps them with allocating resources and making decisions beyond robotics.

Professionals Exploring Robotics and AI/ML Together

If you are someone who is looking for IIT kharagpur courses for working professionals and exploring AI/ML with robotics, this program fits them best and cover the progression from robot fundamentals and control to perception, deep learning, and autonomous systems.

IIT Kharagpur Executive Post Graduate Certificate in Applied Robotics and AI/ML Fees Structure

The cost of this Executive Post Graduate Certification in AI And Machine Learning with Applied Robotics is below: