I am a PhD student in the Paul G. Allen School of Computer Science & Engineering school at the University of Washington, Seattle. I am a part of the Robotics and State Estimation Lab directed by Dieter Fox. My research interests are in Robotics, Computer Vision and Machine Learning. Specifically, I am interested in the intersection between modern machine learning techniques such as deep learning and their applications to problems in robotics and vision such as manipulation, visuomotor control, learning from demonstration, video prediction and intuitive physics.

Prior to joining UW, I did a Masters in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania, where I was a member of the Kodlab, working with Daniel Koditschek and Shai Revzen. I got my undergrad from the Mechanical Engineering department at the College of Engineering-Guindy, India.



A. Byravan, F. Leeb, F. Meier & D. Fox, SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control, Submitted to the IEEE International Conference on Robotics and Automation (ICRA-2018) [arXiv] [Project] [Video]

A. Byravan & D. Fox, SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks, IEEE International Conference on Robotics and Automation (ICRA-2017) (Best vision paper finalist) [arXiv] [PDF] [Video]

J. Mainprice, A. Byravan, D. Kappler, D.Fox, S. Schaal & N. Ratliff, Functional Manifold Projections in Deep-LEARCH, Workshop on Neurorobotics, Neural Information Processing Systems (NIPS-2016) (Best workshop paper) [pdf]

Z. Marinho, A. Dragan, A. Byravan, B. Boots, S. Srinivasa & G. Gordon, Functional Gradient Motion Planning in Reproducing Kernel Hilbert Spaces, Robotics: Science and Systems (RSS-2016) [arXiv]

A. Byravan, M. Monfort, B. Ziebart, B. Boots, & D. Fox, Graph-based Inverse Optimal Control for Robot Manipulation., Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI-2015) [PDF] [Video]

B. Boots, A. Byravan, & D. Fox, Learning Predictive Models of a Depth Camera & Manipulator from Raw Execution Traces., Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA-2014) [PDF]

A. Byravan, B. Boots, S. Srinivasa, & D. Fox, Space-Time Functional Gradient Optimization for Motion Planning., Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA-2014) [PDF]


  • I co-organized the workshop on Learning from Demonstration in High-Dimensional Feature Spaces at the 2017 conference on Robotics: Science and Systems (RSS) with Jim Mainprice (MPI), Mathew Monfort (MIT), Roberto Calandra (UC Berkeley) and Stefan Schaal (USC, MPI)