I focus on the intersection between control in Robotics and Reinforcement Learning. My aim is to develop algorithms and techniques with the help of which machines can autonomously acquire the skills for executing complex tasks.
[Linkedin]
[Google Scholar]
[Research Gate]
Indian Institute Of Technology Kharagpur | 2019 - Present
CGPA-8.09
MDS SCHOOL | 2016 - 2018
Percentage-86.8
MAHARANA MEWAR PUBLIC SCHOOL | Till 2016
CGPA-10.00
Mila - Quebec AI Institute | May 2022 - August 2022
Work focuses on Optimisation and Reinforcement learning
Carried out an empirical study to understand the effects of different optimizers and their critical gradient versions on Atari/Atari100k using Rainbow DQN Algorithm.
Symbiosis Centre for Applied Artificial Intelligence | September 2020 - January 2023
Work focuses on Robotics, Reinforcement learning and Allied areas.
Created a sample efficient state representation learning framework (ShivNet) from the observations
Incorporated Reinforcement Learning with ShivNet for Self-Supervised Robotic Manipulation tasks like lifting, grasping of objects, opening and closing drawer.
DeepLearning.AI | August 2021 - September 2022
Resolving doubts and issues of students who take courses on Deeplearning.ai/Coursera
Jan Elaaj Healthcare (P) Ltd. | 2021 - Present
Work focus on using image/signal processing techniques from computer vision to develop health care product.
September 2022
Rahul Choudhary, Rahee Walambe, Ketan Kotecha
[Paper]
[Site]
06 2020 - 04 2021
Implemented basic Deep Reinforcement Learning algorithms like REINFORCE, SARSA, DQN, A2C to advanced algorithms like DQN with Prioritised Experience Replay and target networks, PPO extending Actor- Critic Algorithm, SAC,A3C, Noisy Nets,Rainbow in various easy and medium level difficult gym environments.
Codes were written from scratch in tensorflow framework in python.Reproduced approximately 70-80 of the level of accuracy provided in the research papers that introduced the algorithms.
[Github]
10 2020 - 11 2020
Implemented Time-Contrastive Networks and Soft-Actor Critic to form a pipeline of inverse reinforcement learning to perform robotic grasping.
The robot is able to learn to pick up objects similar in shape but different in size from only one video demonstration of human picking up the block.
06 2021 - 06 2021
Utilized the Twiddle algorithm to optimise parameters for a PD Controller and combined with a Smoother to make a robotic car reach its goal on a smooth path without colliding.
Implemented a particle filter for localisation of the car and A* Algorithm to find optimal path.