Roshan Dhakal

I'm a Ph.D. student researching at the intersection of Robotics and Machine Learning in Robotic Anticipatory Intelligence & Learning (RAIL) Lab advised by Dr. Greg Stein at George Mason University (GMU), Fairfax, VA. I received my Masters degree in Computer Science from GMU in 2021. I completed Bachelors degree in Computer Engineering from Tribhuvan University, Nepal.

Feel free to contact me at dhakalrosan@gmail.com.

Email / Resume / Scholar / Github

News

(August 2023) I continued as Graduate Teaching Assistant for graduate level Data Mining Course at GMU (CS 584).
(May 2023) I presented Anticipatory Planning paper in ICRA 2023, London, UK.
(January 2023) Our paper on Anticipatory Planning got accepted at ICRA 2023.
(May 2021) Earned M.S. in Computer Science (Machine Learning concentration) from George Mason University.
(May 2021) Successfully defended my Ph.D. comprehensive exam.

Research

I envision a future where household and service robots will coexist with humans, undertaking complex tasks. The focus of my research is to comprehend not only the methods a robot employs to execute a task but also the impacts of its actions. My objective is to equip robots with the ability to understand the consequences of their actions for task and motion planning (TAMP) problems and take actions intelligently. This includes estimating future outcomes and making informed decisions, even in the absence of complete knowledge about their surroundings.

My research intersects robotics and machine learning focusing on task and motion planning, learning augmented planning and lifelong robot planning. Below are some of my works.

	Anticipatory Planning: Improving Long-Lived Planning by Estimating Expected Cost of Future Tasks. Roshan Dhakal, Md Ridwan Hossain Talukder, Gregory J. Stein ICRA, 2023 video / bibtex We propose anticipatory task planning to enable a robot consider unseen future tasks while solving a given task.
	Room Classification on Floor Plan Graphs using Graph Neural Networks. Abhishek Paudel, Roshan Dhakal, Sakshat Bhattari arxiv, 2021 bibtex / code We present our approach to improve room classification task on floor plan maps of buildings by representing floor plans as undirected graphs and leveraging graph neural networks to predict the room categories.

Stole website template from Jon Barron source code.