I'm

PhD Student@ IISc Bangalore, IIT Bombay Graduate, Robotics Engineer

Hi! I am Manan, a Research Scholar at Robert Bosch Centre for Cyber Physical Systems (RBCCPS) in Indian Institute of Science (IISc) Bangalore, working on Control and Design of Walking Robots in the Stochastic Robotics Lab under the guidance of Prof. Shishir N. Y. Kolathaya and Prof. Ashitava Ghoshal and collaborating with Prof. Ayonga Hereid (Ohio State University)

Currently, my work is focussed towards the development of a lightweight control framework for robust proprioceptive bipedal walking.

I completed my B.Tech in Mechanical Engineering from Indian Institute of Technology Bombay (IITB). I actively work in the fields of Robotics, Controls and Learning. In my undergraduate years, I have also worked on various robotics projects like control of Quadruped Bot, Force controlled gripper, FLORENCE (A Robotic Nurse), etc.

My Resume

My Projects

- All
- Robotics
- AI
- Mechanical Design

Research

Design, Learning and Control of Legged Robots: The goal of the research is to design a low cost, affordable Bipedal Robot and to identify a minimum possible control framework that can be deployed to realise stable locomotion behaviour on challenging terrains

Have used Arms to stabilize the locomotion of Bipedal Robot while Walking and Hopping by using Learning Based control of Arm Motions and Swings

Need for unconstrained methods in solving constrained problems. Necessary conditions of unconstrained optimization, Structure of methods, quadratic models. Methods of line search, Armijo-Goldstein and Wolfe conditions for partial line search. Global convergence theorem, Steepest descent method. Quasi-Newton methods: DFP, BFGS, Broyden family. Conjugate-direction methods: Fletcher-Reeves, Polak-Ribierre. Derivative-free methods: finite differencing. Restricted step methods. Methods for sums of squares and nonlinear equations. Linear and Quadratic Programming. Duality in optimization.

Probability spaces, conditional probability, independence, random variables, distribution functions, multiple random variables and joint distributions, moments, characteristic functions and moment generating functions, conditional expectation, sequence of random variables and convergence concepts, law of large numbers, central limit theorem, stochastic processes, Markov chains, Poisson process.

Semester 1

1. Microprocessor system 2. Interfacing physical devices 3. Control system basics 4. EMI/EMC considerations 5. Network connectivity

Semester 1

Fields and linear equations over fields, Vector spaces : Definition, basis and dimension, direct sums. Linear transformations: definition, the Rank-Nullity Theorem, the algebra of linear transformations. Dual spaces. Determinants. Eigenvalues and Eigenvectors, the characteristic polynomial, the Cayley-Hamilton Theorem, the minimal polynomial, and algebraic and geometric multiplicities. Diagonalization. Geometric Algebra

Semester 2

Robot dynamics and kinematics, nonlinear control and stability, Lyapunov theory, PD control, reinforcement learning, imitation learning, model-based and model-free methods, impedance control, trajectory optimization, online learning.

Semester 2

Introduction to reinforcement learning, introduction to stochastic dynamic programming, finite and infinite horizon models, the dynamic programming algorithm, infinite horizon discounted cost and average cost problems, numerical solution methodologies, full state representations, function approximation techniques,approximate dynamic programming, partially observable Markov decision processes, Q-learning, temporal difference learning, actor-critic algorithms.

Semester 2

Introduction to pattern recognition, Bayesian decision theory, supervised learning from data, parametric and non parametric estimation of density functions, Bayes and nearest neighbor classifiers, introduction to statistical learning theory, empirical risk minimization, discriminant functions, learning linear discriminant functions, Perceptron, linear least squares regression, LMS algorithm, artificial neural networks for pattern classification and function learning, multilayer feed forward networks, backpropagation, RBF networks, deep neural Networks, support vector machines, kernel based methods, feature selection and dimensionality reduction methods