Mtech RAS

MTech (Robotics and Autonomous Systems) program at a glance

Total number of credits: 64 (11 Core + 15 Specialization and softcore + 12 Electives+ 26 Project credits)
38 course credits (11 Core+ 15 Specialization and softcore + 12 Electives)
26 Credits – Project

Course Registration
Note:
  • 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

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
Robotics Systems
  • CP232 (Aug): Swarm Robotic Systems
  • CP321 (Aug): Imitation Learning for Robotics
  • CP260 (Jan): Robotic Perception
  • CP315 (Jan): Robot Learning & Control
  • CP230 (Jan): Motion Planning for Autonomous Systems
  • E1277 (Jan): Reinforcement Learning
  • MN204 (Jan): Human Machine Interaction for Manufacturing
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

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.
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

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