Nadun Ranawaka Arachchige

I'm a second-year CS PhD student at Georgia Tech, advised by Prof. Danfei Xu, in the Robot Learning and Reasoning Lab. I also have a undergraduate and master's degree in CS from Georgia Tech.

My research interests center on making robot learning feasible for industrial applications by improving accuracy, throughput and safety. Previously, I worked at the Intelligent Sustainable Technologies Division of the Georgia Tech Research Institute on applied robotics and virtual reality for agricultural automation.

CV / Scholar / LinkedIn

News

[Sep 2025] I will be interning at NVIDIA with the GEAR team.
[Aug 2025] Our paper SAIL got accepted to CoRL 2025 with an oral presentation!
[May 2024] I started my PhD at Georgia Tech!
[Jul 2023] Joined ISTD as a Robotics Engineer.

Research (representative works are highlighted)

	SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies Nadun Ranawaka Arachchige, Zhenyang Chen, Wonsuhk Jung, Woo Chul Shin, Rohan Bansal, Pierre Barroso, Yu Hang He, Yingyang Celine Lin, Benjamin Joffe, Shreyas Kousik^†, Danfei Xu^† CoRL, 2025 (Oral Presentation) project page / arXiv System to execute imitation learning policies faster than human demonstrations.
	Joint Model-based Model-free Diffusion for Planning with Constraints Wonsuhk Jung, Utkarsh A. Mishra, Nadun Ranawaka Arachchige, Yongxin Chen^†, Danfei Xu^†, Shreyas Kousik^† CoRL, 2025 project page A unified diffusion framework for planning and optimization with constraints.
	RAIL: Reachability-based Imitation Learning for Safe Policy Execution Wonsuhk Jung, Dennis Anthony, Utkarsh A. Mishra, Nadun Ranawaka Arachchige, Matthew Bronars, Danfei Xu^†, Shreyas Kousik^† ICRA 2025* project page / arXiv Plug-and-play safety filters for policy models.
	What Matters in Learning from Large-Scale Datasets for Robot Manipulation Vaibhav Saxena, Matthew Bronars, Nadun Ranawaka Arachchige, Kuancheng Wang, Woo Chul Shin, Soroush Nasiriany, Ajay Mandlekar^†, Danfei Xu^†, ICLR 2025 project page / arXiv / code MimicLabs, a large-scale data generation framework and data composition study for imitation learning.
	The Utility of Explainable AI in Ad Hoc Human-Machine Teaming Rohan Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige, Reed Jensen, Matthew Gombolay NeurIPS, 2021 arXiv How xAI impacts human-machine teaming.

Design from Jon Barron's website.