Prior to beginning graduate studies, I was a system engineer at Motorola Solutions’ Upgrade Operations, performing system upgrades to critical radio infrastructure.
My research interests are targeted towards high performance system-level optimizations in order to make advanced robotics more viable. I am particularly interested in ways to leverage GPU hardware resources for accelerating sampling-based and learning-based robotics, and more generally integrating bleeding-edge computing paradigms into the domain of physical AI. In addition, I also aim to develop more robust tools tailored to effective hardware accelerated robotics research.
My broader interests in Computer Science include machine learning, system level software engineering, computational statistics and linear algebra, and GPU related IT infrastructure.
2025
arXiv
Foam: A Tool for Spherical Approximation of Robot Geometry
Many applications in robotics require primitive spherical geometry, especially in cases where efficient distance queries are necessary. Manual creation of spherical models is time-consuming and prone to errors. This paper presents Foam, a tool to generate spherical approximations of robot geometry from an input Universal Robot Description Format (URDF) file. Foam provides a robust preprocessing pipeline to handle mesh defects and a number of configuration parameters to control the level and approximation of the spherization, and generates an output URDF with collision geometry specified only by spheres. We demonstrate Foam on a number of standard robot models on common tasks, and demonstrate improved collision checking and distance query performance with only a minor loss in fidelity compared to the true collision geometry. We release our tool as an open source Python library and containerized command-line application to facilitate adoption across the robotics community.
@misc{coumar2025foam,title={Foam: A Tool for Spherical Approximation of Robot Geometry},author={Coumar, Sai and Chang, Gilbert and Kodkani, Nihar and Kingston, Zachary},year={2025},eprint={2503.13704},archiveprefix={arXiv},primaryclass={cs.RO},}
arXiv
Evaluating Machine Learning Approaches for ASCII Art Generation
Generating structured ASCII art using computational techniques demands a careful interplay between aesthetic representation and computational precision, requiring models that can effectively translate visual information into symbolic text characters. Although Convolutional Neural Networks (CNNs) have shown promise in this domain, the comparative performance of deep learning architectures and classical machine learning methods remains unexplored. This paper explores the application of contemporary ML and DL methods to generate structured ASCII art, focusing on three key criteria: fidelity, character classification accuracy, and output quality. We investigate deep learning architectures, including Multilayer Perceptrons (MLPs), ResNet, and MobileNetV2, alongside classical approaches such as Random Forests, Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN), trained on an augmented synthetic dataset of ASCII characters. Our results show that complex neural network architectures often fall short in producing high-quality ASCII art, whereas classical machine learning classifiers, despite their simplicity, achieve performance similar to CNNs. Our findings highlight the strength of classical methods in bridging model simplicity with output quality, offering new insights into ASCII art synthesis and machine learning on image data with low dimensionality.
@misc{coumar2025ascii,title={Evaluating Machine Learning Approaches for {ASCII} Art Generation},author={Coumar, Sai and Kingston, Zachary},year={2025},eprint={2503.14375},archiveprefix={arXiv},primaryclass={cs.GR},}
