MTech RAS Curriculum 2022

Program Plan at a Glance

Total number of credits: 64 (14 Core + 18 Soft Core + 6 Electives+ 26 Thesis Project)

Important Note:

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

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

6 Electives credits-students can take any courses across the departments for these 6 credits, with the consent of the Guide

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)Core

Assignment of Thesis Project Advisor

Summer (May-Jun)


CP282 (1:2)(Soft 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. 14 Credits are from the mandatory (Core) courses
  2. List of Soft-Core courses is below
  3. 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



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

This is an interdisciplinary course on the design of cyber-physical systems, inviting students from all the departments. It provides an in-depth exposure to various elements of a CPS: the microprocessor, memory and IO devices. The course also includes interfacing sensors and actuators and implementing a control loop.  This course uses a practical approach and involves significant C programming on embedded hardware.


Bharadwaj Amrutur/ 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 potentially 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



Data Science for Smart City Applications

Data types (spatio-temporal data, event data, trajectories, time-series, point-reference etc.), data pre-processing (filtering, discretization, standardization, transformation, Imputation etc.),  Regression (linear regression, passion regression), spatio-temporal estimation (kriging, Gaussian process regression etc.), data dissimilarity measures, Pattern discovery (frequent pattern mining, clustering (event, time-series, trajectory clustering, spatio-temporal clustering etc.), Classification (logistic regression, Bayesian classification, SVM, Ensembles), Anomaly/Outlier Detection Techniques, Concepts for big data mining and visualizations (sampling techniques, dimension reduction (PCA, Manifold learning, Self-organizing maps etc.)), Concepts for stream data mining, MapReduce framework.Throughout the course, students will learn to use real data to solve smart city application problems via data science techniques covered in this course.


Punit Rathore



Experimental Techniques for Robotics& automation

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


Bharadwaj Amrutur/Ashish Joglekar/Naveen Arulselvan/Kaushik Sampath


Changed from core to soft core


Field Robotics

Competition Style, Team Based Robotics Project


Bharadwaj Amrutur/Suresh Sundaram/Pushpak Jagtap

Aug-Dec/ Summer(May-Jun)


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 Outcomesof 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 neededin one of (EE, EC, ME, AE, IN, CS)
  • Sponsored Candidates: Selection as per institute norms
  • Selection based on : Gate (70%) + Interview (30%)


Intake as per IISc norms and the sponsored students are also taken according to the rules & regulations