MTech RAS Curriculum 2021

Program Plan at a Glance 

Total number of credits: 64 (17 Core + 21 Soft Core + 26 Thesis Project) 

Important Note:

17 core credits must be completed only from the core courses listed here

21 soft core credits must be completed only from the list given below under the title-Soft Core Courses & Institute Electives

26 credits for the mandatory MTech Project CP 299





Sem 1 (Aug-Dec) 

E1241 (3) 


CP220 (3) (Core) 

CP214 (3:1) (Core) 

 Soft Core (3) 



Sem 2 (Jan-Apr) 

CP230 (2:1) (Core) 

Soft Core (3) 

Soft Core (3) 

Soft Core (3)  

CP210 (0:1) 

Assignment of Thesis Project Advisor 

Summer (May-Jul) 




CP280 (1:2) (Core) 

Thesis Project (3) 


Sem 3 (Aug-Dec) 

Soft Core/Elective (3) 

Soft Core/Elective (3) 



Thesis Project (8) 

Placement Interviews 

Sem 4 (Jan-Apr) 

Soft Core/Elective (3) 




Thesis Project (15) 



  1. 17 Credits are from the mandatory (Core) courses  
  1. List of Soft Core courses is below 
  1. Electives can be any course offered in IISc (with due permission of the instructor)  

Final project will focus on research or technology innovations for industrial or research problems, and will include a mix of analysis, design and implementation. 

Program Details 

Note: Explanation of the credits 

a:b => ‘a’ hours/week of lectures and  ‘b’x 3 hours/week of laboratory work. 

Core Courses: 

Course No 

Course Name 

Course Contents in Brief 






Dynamics of Linear Systems 

State space modeling, Linear Systems, Linear Control  


Vaibhav Katewa 


CP 220 

Mathematical Techniques  

Intro to Probability, Linear Algebra, Bayesian Inference, Introduction to Optimization 


Bharadwaj Amrutur 



Autonomous Navigation & Planning 

Navigation and planning for autonomous robots 


Debasish Ghose/Mukunda 



Experimental Techniques for Robotics& automation 

ROS/ROS2, SLAM, Networking, Navigation, Planning, Competition Project 


Bharadwaj Amrutur/Ashish Joglekar/Naveen Arulselvan/Kaushik Sampath 



Foundations of Robotics 

Modelling and simulation of robots 


Shishir NY  




Exposure to latest topics in research and industry 


Ashitava Ghosal 



Soft Core courses & Institute Electives: 

Course No 

Course Name 

Course Content 





Applied Optimal Control and State Estimation 

Calculus of Variations & optimal control formulation, Two-point boundary value problems, LQR, STM, SDRE, HJB theory, MPSP design & extension etc 


Radhakant Padhi 



Nonlinear Systems and Control 

Equilibria and qualitative behavior, Existence and uniqueness of solutions,Lyapunov stability and related ideas, control design, applications 


Pavan T 



Topics in Networked and Distributed Control 

Relevant background topics in control, Estimation & control under communication constraints, event triggered control, connectivity maintenance, security in networked & distributed control systems, applications in robotics & transportation 


Vaibhav L./Pavan T 



Reinforcement Learning 

MDP, Reinforcement Learning 


Shalabh B/Gugan Thoppe 

Jan – Apr 


Formal Methods in Computer Science 

Model checking, program verification 


Deepak D. 



Design for IoT 

Introduction to IoT, Challenges in IoT – Power, Security, Identification, Location, Low Power Design, Energy harvesting systems, Power management algorithms, ARM processor low power features, multiprocessor systems, Lifetime estimation, RFID and its applications, backscattering techniques, Working with protocols such as MQTT, COAP, for low power and energy harvesting sensor nodes, Low power wireless networks – Bluetooth Low Energy (BLE), and IEEE 802.15.4e TSCH.  Low Power Wide Area Networks – LORA, NBIoT and power-saving modes, CAT-LTE-M1. 


T V Prabhakar 



Design of CPS -II 

C/C++, Realtime OS, Embedded Programming 


Darshak Vasavada/Ashish Joglekar 


Replaced by another course CP 316-Real time embedded systems 


Design of CPS-I 

Sensor Front ends, Actuators, Motors and Motor Drives, EMI/EMC, Sensor Noise and data conversion, Imaging/acoustic/infrared/lidar/radar transducers 


Ashish Joglekar/Darshak Vasavada 



Swarm Robotic System 

Autonomous operation of Drone/Robots, Swarm Intelligence (Self-Organization & emergence). Motion control, planning, target tracking, predator-prey, formation, Cooperation and Coordination, Market, team theory, game theoretic approaches, decision making under uncertainty 


Suresh Sundaram/Jishnu Keshavan 

Jan – Apr 

Changed to Aug-Dec 


Human-Computer Interactions 

Basic psychology of Perception and Motor Action, Collaborative Robots, Introduction to AR/VR and Haptics systems, Facial Expression Recognition, Case studies on HRI 


Pradipta Biswas 


CP 314 

Robot Learning and Control 

Machine learning based control for robotic systems 


Shishir NY 


This one to be retained for sake of old students 


CP 315 

Robot Learning and Control 

Machine learning based control for robotic systems 


Shishir NY 


New one with reduced credits 


Perception & Intelligence 

Localization & Mapping, Multi-Sensor Perception, Knowledge Representation, Reasoning 


Bharadwaj Amrutur/Raghu K 



Industrial IoT systems  

Industrial protocols such as Time Triggered Ethernet, Time Sensitive Networks, Detnets, Modbus/TCP, PLC systems, Construction of Digital twins, Condition monitoring, 


TV Prabhakar 



Human Robot Interactions 

Introduction; Cross-disciplinary foundation; Hardware and Software components and architecture; Research themes; Building blocks; Navigation, Interaction, Manipulation, and Behavioral aspects for Social Robots; AI for social robots (including Autonomy and Learning for Social Intelligence); Designing a social robot (including Humanoid, mobile and interactive robots); User Studies; Pointers to advanced Topics in the domain. 


Pradipta Biswas/Amit Pandey/Sridatta Chatterjee 



Formal Analysis and Control of Autonomous Systems 

This course will provide an end-to-end overview of different topics involved in designing or analyzing autonomous systems. It begins with different formal modeling frameworks used for autonomous systems including state-space representations (difference equations), hybrid automata, and in general labeled transition systems. It also discusses different ways of formally modeling properties of interest for such systems such as stability, invariance, reachability, and temporal logic properties. 

As a next step, the course will cover different techniques on the verification of such systems including Lyapunov functions, reachability, barrier certificates, and potential model checking. Finally, the course will introduce students to several techniques for designing controllers enforcing properties of interest over autonomous systems. 



Pushpak Jagtap 



Real-time Embedded Systems 

The course is organized in three parts: standalone (OS less) systems, multi-tasking systems with RTOS and systems with embedded OS. The course involves significant programming in C on embedded platforms running RTOS / embedded Linux. 

Part 1: Standalone systems: 4 weeks 

Software architecture: control loop, polling and interrupt driven systems, PID control and finite state machine 

Experiments: interfacing sensors and actuators to implement a standalone control system on an ARM based hardware platform. 

Part 2: Multi-tasking systems: 6 weeks 

Introduction to real-time systems, multitasking, scheduling, inter-task communication, memory management and device drivers 

Experiments: build a multitasking system involving multiple simultaneous activities involving computing algorithms, IO processing and a user interface. 

Part 3: Embedded Linux: 4 weeks 

Building an embedded Linux system; processes and threads, memory management, file-system, drivers. Real-time limitations and extensions. 

Experiments: build connected application with sensor/actuator front-end and embedded Linux for UI and connectivity. 



Pushpak Jagtap/Darshak Vasavada 



Theory & Applications of Bayesian Learning 

Descriptive Statistics, Introduction to Probabilities, Bayes Rules, Probability Distributions, Maximum Likelihood Estimation, Bayesian Regression and Classification, Expectation Maximization, Frequentist vs Bayesian Learning, Conjugate Priors, Graph Concepts, Bayesian Belief Networks, Probabilistic Graphical Models (PGMs), Probabilistic and Statistical Inferencing, Bayesian Estimation, Structure Learning, Bayesian Optimization, Markov Random Fields, Markov Chain Monte Carlo, PGM examples and applications (including industry and smart cities applications) 


Punit Rathore 



Field Robotics 

Competition Style, Team Based Robotics Project 


Suresh Sundaram 

Summer (Jun-Jul) 


Detection & Estimation Theory 



Vaibhav Katewa 




Entrepreneurship, Ethics & Societal Impact 


Madhu Atre 



Laboratory Modules (will be embedded in the courses as well as in CP280): 



Mobile Robot Programming 

Learn to program mobile robots to navigate around an obstacle course 

Control of Industrial Robot Arms 

Learn to program industrial robot arms to do various tasks like pick and place, movements, gripping, welding etc. Program collaborative arms. 

Programming of Drone Systems 

Program drones for landing, sorties, pattern flying, etc.  

Tele robotics 

Control robot arms/humanoids over the network. 

Robot simulation frameworks 

Exercises in Simulation frameworks like PyBullet, Gazebo by creating robot and world models, demonstration of various algorithms 

VR/AR & Speech Interfaces 

Programming VR/AR, haptics and speech interfaces to machines/robots 

Human Robot Interaction 

Interfacing Robots with interactive devices like gesture and speech recognition systems, eye gaze tracker, Industrial CoBoT, TeleRobotics, HRI for semi-autonomous vehicle, cognitive load estimation (“human/operator state” might be a broader term?) 

ROS/ROS2 Software Stacks 

Robot programming using ROS/ROS2 

Machine Learning for Robots 

ML based Control for Grasping and Manipulation 

Robot Sensor and Actuator Systems 

Integrate new sensor and actuator system to a robot’s perception system 

3D Design and Prototyping for Robotics 

3D CAD design, URDF model creation, 3D printing of part and attachment to a robot 

Drone piloting 

Learn to fly drones in IISc testbed 

Multi-sensor odometry  

Wheel, IMU, GPS, Lidar, wireless, and combinations by filter-based fusion 

Visual navigation 

Tag-based, landmark-based, feature based, direct methods, deep learning based, monocular and stereo methods 

Relevance and Need of the Program 

Robotics and autonomous systems are an integral part of Industry 4.0 and robotic automation will see an exponential growth in the future. The shortage of skilled labor in transport, agriculture, and supply chain management; operations in hazardous environment like mines and waste processing and recovery; remote monitoring, space explorations and defense will drive the future of robotic automation solutions. Co-design of Cyber and Physical components of such systems and their safe operation along with humans in unstructured and uncertain environments will be key characteristics of such systems. 

Robotics and Autonomous Systems have also changed over the last decade – with the confluence of AI/ML with cheap sensors/Actuators and Battery technologies 

With substantial investments by the Govt of India and Govt. Of Karnataka in this area at IISc through the AI & Robotics Park Initiative (ARTPARK), as well as investments from companies like Cisco, Nokia, Garrett etc. via CSR grants – IISc has started developing state of the art experimental facilities in Connected Robots and Autonomous Systems. These facilities to not only support our research, but also develop rich experiential/laboratory-based training programs for the students. 

Robotics and Autonomous System has always fascinated our UG students as evidenced by their keen participation in many robotic competitions. However, there is no comprehensive course program in this domain in the country that can train then in the foundational aspects as well as the experimental aspects of the subject.  

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.  


RBCCPS under Div. Of Inter-Disciplinary Sciences, is in a unique intersection of Div. Of EECS and Mechanical Sciences and hence can offer such training spanning both the disciplines – and will be the pillar of this program. ARTPark (AI & Robotics Technologies Park) has been incubated by RBCCPS and will provide laboratory support. 

How will this program benefit Industry? 

Graduates will have a 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 create innovative technologies and products in the emerging AI & Robotics applications in industry 4.0, medical, agriculture, mining, defense, 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 

Target Stakeholder Beneficiaries 

Industries in Robotics and Autonomous Systems like: ABB, Bosch, TCS, Wipro, GE, Defense PSUs, Amazon, Flipkart, Target, Nokia, Google, CAIR, ADA, NAL, Intel, Siemens, etc. and many startups 

Research Labs in academia like in IISc and IITs. 

Laboratory Facilities:  

  • Robot Arms,  
  • Indoor Mobile Robots & Drones,  
  • Outdoor Mobile Robots & Drones,  
  • Outdoor autonomous driving testbed  
  • Indoor Connected Robots Lab  
  • Humanoid Robots,  
  • Legged Platforms & Treadmills,  
  • Indoor Mocap system,  
  • Indoor Drone Testbed, Windshaping Facility & Indoor Drones,  
  • Ware-house Robotics Testbed,  
  • Electronics and Mechanical Prototyping Facilities 


As per IISc Norms  


  • Background Degree: BE, BTech, BS (4years)/Equivalent with Gate about cutoff. 
  • For Ministry of Education (MOE) Scholarship: GATE needed in one of (EE, EC, ME, AE, IN, CS) 
  • Sponsored Candidates: Selection as per institute norms 
  • Selection based on : Gate (70%) + Interview (30%) 


30/year for first two years – increasing to 50/year post that. Allot 10% for sponsored students.