Mara Levy

I am a PhD student in the Computer Science department at the University of Maryland, College Park. I work on robotic learning and computer vision with Abhinav Shrivastava. I graduated Summa Cum Laude from the University of Pennsylvania where I majored in Computer Science and minored in Mathematics.

Email / CV / Scholar / Github

Research

My research utilizes a mixture of computer vision and robotics approaches to try and make robots more useful in the real world. Currently I am interested in how robots can learn from a very small number of demonstrations. In the long term I am interested in using these techniques to learn from online videos of humans. These learned skills can then be combined for long term planning.

	ARDuP: Active Region Video Diffusion for Universal Policies Shuaiyi Huang, Mara Levy, Zhenyu Jiang, Anima Anandkumar, Yuke Zhua, Linxi Fan, De-An Huang, Abhinav Shrivastava Under Submission A novel method for learning complex goal-conditioned robotics tasks from a single demonstration, using unique reward function and knowledge expansion.
	WayEx: Waypoint Exploration using a Single demonstration Mara Levy, Nirat Saini, Abhinav Shrivastava IEEE International Conference on Robotics and Automation (ICRA), 2024 A novel method for learning complex goal-conditioned robotics tasks from a single demonstration, using unique reward function and knowledge expansion. Project / Paper
	V-VIPE: Variational View Invariant Pose Embedding Mara Levy, Abhinav Shrivastava Conference on Computer Vision and Pattern Recognition (CVPR) RHOBIN Workshop, 2024 A method for projecting poses into an embedding space that is view invariant. This means the same pose shot from different camera angles should have an equal embedding. Paper
	Coarse-to-Fine Human Mesh Recovery with Transformers Vatsal Agarwal, Mara Levy, Max Ehrlich, Youbao Tang, Ning Zhang, Abhinav Shrivastava Under Submission Build efficient Transformer design for non-parametric human mesh recovery with coarse-to-fine pipeline. Paper
	No-frills Dynamic Planning using Static Planners Mara Levy, Vasista Ayyagari, Abhinav Shrivastava IEEE International Conference on Robotics and Automation (ICRA), 2021 A planning algorithm that helps reinforcement learning algorithms that can typically only solve tasks for static objects solve the same task, but with a dynamic object. Project / Paper

Template Credits Jon Barron