Required-11 Credits are from the mandatory Core courses.
Electives can be any course offered in IISc (with due permission of the instructor) – Balance of 12 credits.
Final project -26 credits-CP 299 will focus on research or technology innovations/development for industrial or research problems, and will include a mix of analysis, design and implementation.
Link to the details of courses offered in the August 2024 Semester: Link
Link to the details of courses offered in the January 2025 Semester: Link
List of Core Courses
CP241 (Aug): Applied Linear and Nonlinear Control
CP214 (Aug): Foundation of Robotics
CP217 (Aug): Machine Learning for Cyber-Physical Systems
List of Softcore Courses
A minimum of 15 credits (apart from core courses) is to be mandatorily taken from the list of softcore courses. To earn a specialization in one of the following areas—control systems, robotics systems, or mobility and urban intelligence—you must also complete a minimum of 12 credits in courses specific to that specialization.
Control Systems
E1241 (Aug): Dynamics of Linear Systems
E1246 (Aug): Topics in Networked & Distributed Control
IN227 (Aug): Control System Design
CP275 (Jan): Formal Analysis & Control of Autonomous Systems
E1242 (Jan): Nonlinear Systems and Control
E1244 (Jan): Detection & Estimation Theory
E1248 (Jan): Sliding Mode Control & its Applications
E1277 (Jan): Reinforcement Learning
AE372 (Jan): Applied Optimal Control & State Estimation
CP230 (Jan): Motion Planning for Autonomous Systems
E1277 (Jan): Reinforcement Learning
MN204 (Jan): Human Machine Interaction for Manufacturing
CP321 (Jan): Imitation Learning for Robotics
CP213 (Jan): Hands-on Intro to Robotic Actuators
Mobility and Urban Intelligence
CP331 (Aug): Energy Data Analytics
CP320 (Aug): Operations Research for Mobility Management
CE275 (Aug):Transportation Systems Modelling
CE205A (Aug): Transportation Logistics
E3258 (Aug): Design for Internet of Things
CP330 (Jan): Edge AI
CE271 (Jan): Choice Modeling
CE272 (Jan): Traffic Network Equilibrium
E1277 (Jan): Reinforcement Learning
Other Softcore Courses
CP220 (Aug): Mathematical Techniques for Robotics Systems
CP212 (Aug): Design of Cyber-Physical Systems
DS221 (Aug): Introduction to Scalable Systems
CP218 (Jan): Theory and Applications of Bayesian Learning
CP316 (Jan): Real-time Embedded Systems
E0272 (Jan): Formal Methods in Software Engineering
CP290 (Jan): Commercializing Innovation: The Science and Strategy of Building Successful Deep-tech Startups
MTech Projects
The detailed guidelines and evaluation for MTech projects can be found here.The project reports are expected to be submitted in the IEEE double-column paper format as per the page limits mentioned in guidlines. The templates are available at the links below.MS Word TemplateLatex Template
Key Learning Outcomes of the program
Foundational concepts in Mathematical Foundations like Linear Algebra, Computational Techniques, Probability and Statistics, Control & Optimization, Statistical Signal Processing, Planning & Decisions, Stochastic and Data driven Control, AI for Robotics, Dynamics & Kinematics, Formal Techniques for CPS
Applied concepts in Networking for Robotics & Autonomous Systems, Real-Time Embedded Systems, Sensing & Actuation Systems, Applied Machine Learning for Speech & Vision, Reinforcement Learning for Robot Control, Swarm & Team Robotics, Human-Machine Interactions & social robotics, Security, Safety & Privacy for Autonomous Systems, Autonomous ground/air Robots, Navigation & Guidance, Perception via Signal & Image Processing
Experiential learning via laboratory modules for: Mobile robot programming, Control of Industrial Robot Arms, Programming of Legged Robots, Programming of Drone Systems, Tele-Robotics, Secure Data Pipelines, Robot Simulation Frameworks, VR/AR & Speech Interfaces for Robots, ROS/ROS2 Software stacks, Machine Learning for Robots, Robot hardware for Sensor & Actuator Systems, 3D Design and Prototyping for Robotics, Drone Piloting, Game engine programming, GPU Programming.
How will this program benefit Industry?
Graduates will have good exposure to foundational and applied topics in Robotics and Autonomous Systems.
Hands-on exposure to engineering with robots – including mechanical prototyping, ROS/ROS2 programming, AI/ML based programming, VR/AR and other HCI Technologies, 5G and WiFi6 experimentation, indoor and outdoor ground, and aerial robot experiences, will make them well rounded and prepared for many industry problems.
They will be able to contribute effectively to creating innovative technologies and products in the emerging AI and robotics applications in industry 4.0, medical, agriculture, mining, defence, smart cities etc.
How will this program benefit academia?
Good theoretical and practical grounding will prepare the graduates of this program to participate in innovative experimental research in Robotics and Autonomous Systems
Target Audience
Students with UG/PG background in EE, ECE, CS, Mech, Aero and related fields
