I am an undergraduate student in Computer Science at Purdue University. I am broadly interested in motion planning and hardware optimizations along with deep learning.
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},note={Under Review},}
