Ankit Sinha

Hello 👋, thanks for visiting my profile! I'm a Master's student in Computer Science at the Georgia Institute of Technology, doing robotics research under the guidance of Prof. Sehoon Ha. Prior to this, I worked as a Software Developer at Oracle Cloud Infrastructure. I earned my B.Tech in Computer Science and Engineering from the Indian Institute of Technology (BHU), Varanasi. During my undergraduate studies, I had the opportunity to collaborate closely with Prof. Pratik Chattopadhyay and Prof. Prasenjit Chanak. Feel free to explore my projects and reach out if you'd like to connect!

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Research

My current research interest is in Robot Learning. I like to develop robotic controllers using learning-based methods that can involve Sim-to-Real RL, Offline RL, Imitation Learning, etc. However, I come from a Data Science / ML background with experience in Computer Vision and Natural Language Processing (pre-GPT era). Below, you can find some of my ongoing and past projects.

Developing an IsaacLab extension to simulate and optimize the morphology of the BALLU (Buoyancy-Assisted Legged Locomotion Unit) robot with the goal of achieving efficient and dynamic locomotion.

We implement an end-to-end Deep Learning pipeline consisting of a Faster-RCNN-FPN localizer and a ResNet-18 encoder for product recognition in retail stores.

Projects

Here are some of the projects I've worked on, showcasing my skills in Machine Learning, Data Science, and Reinforcement Learning.

Implemented a Deep Q-Network (DQN) agent that learns to play Video Poker from scratch, and compared its performance against an optimal agent using a brute-force solution. The project explores how closely a model-free RL agent can approximate perfect strategy in a complex, yet solvable game.

PoC of a pipeline that processes scanned documents from arbitrary images and classifies them into their respective categories with 98-99% accuracy. The system combines Computer Vision (OpenCV, Tesseract) with NLP (Word2Vec, GloVe embeddings) and Deep Learning for robust document understanding.