Abhishek Jha

I'm an incoming computer science graduate student at New York University, Courant. I completed my bachelor's from Delhi Technological University in New Delhi. My interests broadly lies in machine learning, computer vision and robotics to develop safe and scalable intellgents systems that can be universally adopted.

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Research

I have a strong interest in multi-agent systems, control theory, visual navigation, computer vision and generative AI, as these areas form the core of my work. My research mainly focuses on improving how intelligent robotic systems navigate. I work on making these systems safe by ensuring they avoid collisions and deadlocks while being scalable to handle more complex environments. I want to create robots that can be trusted to work in real-world settings, whether it’s in autonomous vehicles, factories, or even homes. My goal is to make robots not just smarter but also safer and easier to use, so they can be widely adopted and help people in their everyday lives.

A single decentralized control algorithm using neural ICBFs and gradient optimization that ensures safe, input-constrained, deadlock-free control of 1000+ agents in cluttered environments.

A semi-supervised segmentation model (PV-S3) that detects defects in photovoltaic EL images using only 20% labeled data, outperforming state-of-the-art supervised methods and reducing annotation costs by 80%.

A fine tuned transfer learning model using the SimCLR and SwAV models that predicts autism from resting-state fMRI scans, showcasing the potential of contrastive and non-contrastive models for robust neuroimaging analysis.

Introduced Strategic Pseudo-Goal Perturbation (SPGP), that resolves deadlocks in multi-agent navigation by guiding agents through strategic pseudo-goals, enhancing safety and efficiency in complex scenarios.

A transfer learning model using the Xception ConvNet predicts autism from resting-state fMRI scans, demonstrating the feasibility of early diagnosis through deep learning on brain imaging data.

An integrated ANN and Genetic Algorithm model predicts and optimizes Material Removal Rate and surface roughness in Al 6061 turning operations, enhancing machining precision through simulation-based methods.

Projects

A Benchmark and Survey of Deadlock Resolution in Multi-Robot Navigation in Social Mini-Games

Autonomous navigation and trajectory planning of a robot using Robot Operating System (ROS). A maze is created in gazebo for the robot to determine the best possible trajectory with collison avoidance. Probablistic localization method is used for navigation. Adaptive Monte Carlo Localization(AMCL) node and slam_gmapping package is used for localization of robot and mapping of robot. Rviz interface is used for the simulation of robot and creating the cost map for the travel of robot.

Obstacle avoidance implemented in Robot operating system for Unmanned Aerial Vehicle (UAV) using LiDAR scan data. Hector quadrotor package is used for spawning the drone in the gazebo environment. Readings from SONAR sensor with python script is used for flying the drone to a certain height. LiDAR readings are used to detect obstacles in the surroundings.

Path planning of point robot for finding shortest path between start and goal position. Implemented Informed and Uninformed search algorithms for the path planning problem. A random 2D enivronment is created with obstacles for evealuation of searching algorithms. A* and Dijkstras algorithm is implemented for obtaining the shortest path. Investigated the perofrmance by implementing other search based algorithms such as Best first, Depth First and Breadth First for finding the shortest distance. Given below are the images of search done by the various algorithms.

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