My primary research focus revolves around the exploration of deep learning algorithms and their integration into robotic systems to enhance their intelligence, physical consistency, and seamless interaction with human counterparts. As a Software Engineer at Locus Robotics, I actively contribute to the advancement of autonomous mobile robots by enabling them to perceive their surroundings and make intelligent decisions.
I have recently completed my Master of Science degree in Computer Science from the University of British Columbia, where I had the privilege of being supervised by Ian M. Mitchell. Prior to my enrollment at UBC, I served as a research fellow at TCS Research and Innovation Labs, where I made contributions to the automation of warehouse robotics under the guidance of Swagat Kumar and Rajesh Sinha.
Additionally, I hold a Bachelor’s degree in Computer Science from IIIT Delhi, where I had the opportunity to work under the supervision of Rahul Purandare, and I actively collaborated closely with P.B. Sujit during my tenure.
My pronouns are he/him.
MSc in Computer Science, 2022
University of British Columbia, Vancouver
BTech in Computer Science Engineering, 2017
Indraprastha Institute of Information Technology, Delhi
We propose to augment smart wheelchair perception with the capability to identify potential docking locations in indoor scenes. ApproachFinder-CV is a computer vision pipeline that detects safe docking poses and estimates their desirability weight based on hand-selected geometric relationships and visibility. Although robust, this pipeline is computationally intensive. We leverage this vision pipeline to generate ground truth labels used to train an end-to-end differentiable neural net that is 15 times faster.
ApproachFinder-NN is a point-based method that draws motivation from Hough voting and uses deep point cloud features to vote for potential docking locations. Both approaches rely on just geometric information, making them invariant to image distortions. A large-scale indoor object detection dataset, SUN RGB-D, is used to design, train and evaluate the two pipelines.
Potential docking locations are encoded as a 3D temporal desirability cost map that can be integrated into any real-time path planner. As a proof of concept, we use a model predictive controller that consumes this 3D costmap with efficiently designed task-driven cost functions to share human intent. This wheelchair navigation controller outputs a nominal path that is safe, goal-oriented and jerk-free for wheelchair navigation.
Designed and graded questions for homework assignments, quizzes and examinations for the following courses:
I was involved in multiple research projects for warehouse automation using industrial manipulators. Specifically, I have worked on 3D pose-estimation heterogenous sized boxes using pointclouds and motion of planning for
Universal Robots using
ROS.
Following is a list of selected projects that I worked on:
Please refer to my Curriculum Vitae for a detailed description of these projects.
Multiple tutorials covering how to implement visionfocused deep learning architectures in PyTorch with torchvision.
Developed an end-to-end docking location detection network based on synergy of deep point set networks and Hough voting.
Developed a real-time computer vision pipeline to find potential docking locations indoor environments for wheelchairs using point cloud data.
Real-time wheelchair navigation with shared control using model predictive path integral (MPPI) controller.
Indoor object detection using Votenet for pointclouds captured from RGB-D cameras in ROS simulation.
Image-based visual servoing in eye-in-hand configuration for Universal Robot 5 using Microsoft Kinect V2 camera.
Developed a predictive model that can play chess like humans, with special focus on modelling amateur play.
Summarised 10 state-of-the-art approaches to verify DNN and developed a framework to test networks (eg ACAS Xu) on safety cases using SMT solvers.
Developed a CNN capable of obtaining a temporally consistent, full 3D skeletal human pose from a single RGB camera.
Converted Sudoko as a Boolean Satisfiability Problem to solve it through SAT
Studied and summarised major approaches to perform text detection and recognition using deep learning techniques.
Developed an optimal path planning algorithm in obstacle rich environments. BugFlood unlike its predecessor uses a split and kill approach to advance in the environment. Performance of this algorithm was compared with different planners from Open Motion Planning Library (OMPL) and visibility graph methods.
Developed a static analysis Clang based tool for ROS to reduce network latency and dropout rate by optimizing message size.
A clang based tool to find different types of statements in C/C++ code. This tool is used generate meta-data for a ROS package.
Papers, Workshops and Patents